Volitional down-regulation of the primary auditory cortex via directed attention mediated by real-time fMRI neurofeedback

Matthew S. Sherwood; Jason G. Parker; Emily E. Diller; Subhashini Ganapathy; Kevin Bennett; Jeremy T. Nelson; Matthew S. Sherwood; Jason G. Parker; Emily E. Diller; Subhashini Ganapathy; Kevin Bennett; Jeremy T. Nelson

doi:10.3934/Neuroscience.2018.3.179

AIMS Neuroscience

2018, Volume 5, Issue 3: 179-199. doi: 10.3934/Neuroscience.2018.3.179

Previous Article Next Article

Research article

Volitional down-regulation of the primary auditory cortex via directed attention mediated by real-time fMRI neurofeedback

1.
Department of Biomedical, Industrial & Human Factors Engineering, Wright State University, Dayton, OH, USA
2.
Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indiana University, IN, USA
3.
School of Health Sciences, Purdue University, West Lafayette, IN, USA
4.
Department of Trauma Care, Boonshoft School of Medicine, Wright State University, Dayton, OH, USA
5.
Department of Psychology, Wright State University, Dayton, OH, USA
6.
Department of Defense Hearing Center of Excellence, JBSA-Lackland, USA

Received: 27 October 2017 Accepted: 25 June 2018 Published: 07 July 2018

The present work assessed the efficacy of training volitional down-regulation of the primary auditory cortex (A1) based on real-time functional magnetic resonance imaging neurofeedback (fMRI-NFT). A1 has been shown to be hyperactive in chronic tinnitus patients, and has been implicated as a potential source for the tinnitus percept. 27 healthy volunteers with normal hearing underwent 5 fMRI-NFT sessions: 18 received real neurofeedback and 9 sham neurofeedback. Each session was composed of a simple auditory fMRI followed by 2 runs of A1 fMRI-NFT. The auditory fMRI alternated periods of no auditory with periods of white noise stimulation at 90 dB. A1 activity, defined from a region using the activity during the preceding auditory run, was continuously updated during fMRI-NFT using a simple bar plot, and was accompanied by white noise (90 dB) stimulation for the duration of the scan. Each fMRI-NFT run alternated “relax” periods with “lower” periods. Subjects were instructed to watch the bar during the relax condition and actively reduce the bar by decreasing A1 activation during the lower condition. Average A1 de-activation, representative of the ability to volitionally down-regulate A1, was extracted from each fMRI-NFT run. A1 de-activation was found to increase significantly across training and to be higher in those receiving real neurofeedback. A1 de-activation in sessions 2 and 5 were found to be significantly greater than session 1 in only the group receiving real neurofeedback. The most successful subjects reportedly adopted mindfulness tasks associated with directed attention. For the first time, fMRI-NFT has been applied to teach volitional control of A1 de-activation magnitude over more than 1 session. These are important findings for therapeutic development as the magnitude of A1 activity is altered in tinnitus populations and it is unlikely a single fMRI-NFT session will reverse the effects of tinnitus.
- fMRI,
- neurofeedback,
- neuromodulation,
- primary auditory cortex,
- attention,
- tinnitus
Citation: Matthew S. Sherwood, Jason G. Parker, Emily E. Diller, Subhashini Ganapathy, Kevin Bennett, Jeremy T. Nelson. Volitional down-regulation of the primary auditory cortex via directed attention mediated by real-time fMRI neurofeedback[J]. AIMS Neuroscience, 2018, 5(3): 179-199. doi: 10.3934/Neuroscience.2018.3.179

Related Papers:

Abstract

The present work assessed the efficacy of training volitional down-regulation of the primary auditory cortex (A1) based on real-time functional magnetic resonance imaging neurofeedback (fMRI-NFT). A1 has been shown to be hyperactive in chronic tinnitus patients, and has been implicated as a potential source for the tinnitus percept. 27 healthy volunteers with normal hearing underwent 5 fMRI-NFT sessions: 18 received real neurofeedback and 9 sham neurofeedback. Each session was composed of a simple auditory fMRI followed by 2 runs of A1 fMRI-NFT. The auditory fMRI alternated periods of no auditory with periods of white noise stimulation at 90 dB. A1 activity, defined from a region using the activity during the preceding auditory run, was continuously updated during fMRI-NFT using a simple bar plot, and was accompanied by white noise (90 dB) stimulation for the duration of the scan. Each fMRI-NFT run alternated “relax” periods with “lower” periods. Subjects were instructed to watch the bar during the relax condition and actively reduce the bar by decreasing A1 activation during the lower condition. Average A1 de-activation, representative of the ability to volitionally down-regulate A1, was extracted from each fMRI-NFT run. A1 de-activation was found to increase significantly across training and to be higher in those receiving real neurofeedback. A1 de-activation in sessions 2 and 5 were found to be significantly greater than session 1 in only the group receiving real neurofeedback. The most successful subjects reportedly adopted mindfulness tasks associated with directed attention. For the first time, fMRI-NFT has been applied to teach volitional control of A1 de-activation magnitude over more than 1 session. These are important findings for therapeutic development as the magnitude of A1 activity is altered in tinnitus populations and it is unlikely a single fMRI-NFT session will reverse the effects of tinnitus.

References

[1]	Hirsch J, Ruge MI, Kim KH, et al. (2000) An integrated functional magnetic resonance imaging procedure for preoperative mapping of cortical areas associated with tactile, motor, language, and visual functions. Neurosurgery 47: 711–722.
[2]	Yoo SS, Fairneny T, Chen NK, et al. (2004) Brain computer interface using fMRI: Spatial navigation by thoughts. Neuroreport 15: 1591–1595. doi: 10.1097/01.wnr.0000133296.39160.fe
[3]	Sorger B, Reithler J, Dahmen B, et al. (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
[4]	Yoo JJ, Hinds O, Ofen N, et al. (2012) When the brain is prepared to learn: Enhancing human learning using real-time fMRI. Neuroimage 59: 846–852. doi: 10.1016/j.neuroimage.2011.07.063
[5]	Weiskopf N, Veit R, Erb M, 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
[6]	Mak JN, Wolpaw JR (2009) Clinical applications of brain-computer interfaces: Current state and future prospects. IEEE Rev Biomed Eng 2: 187–199. doi: 10.1109/RBME.2009.2035356
[7]	Logothetis NK, Pauls J, Augath M, et al. (2001) Neurophysiological investigation of the basis of the fMRI signal. Nature 412: 150–157. doi: 10.1038/35084005
[8]	Longden TA, Dabertrand F, Koide M, et al. (2017) Capillary K⁺-sensing initiates retrograde hyperpolarization to increase local cerebral blood flow. Nat Neurosci 20: 717–726. doi: 10.1038/nn.4533
[9]	Hamilton JP, Glover GH, Hsu JJ, et al. (2011) Modulation of subgenual anterior cingulate cortex activity with real-time neurofeedback. Hum Brain Mapp 32: 22–31. doi: 10.1002/hbm.20997
[10]	Zotev V, Krueger F, Phillips R, et al. (2011) Self-regulation of amygdala activation using real-time fMRI neurofeedback. PLoS One 6: e24522. doi: 10.1371/journal.pone.0024522
[11]	Caria A, Veit R, Sitaram R, et al. (2007) Regulation of anterior insular cortex activity using real-time fMRI. Neuroimage 35: 1238–1246. doi: 10.1016/j.neuroimage.2007.01.018
[12]	Veit R, Singh V, Sitaram R, et al. (2012) Using real-time fMRI to learn voluntary regulation of the anterior insula in the presence of threat-related stimuli. Soc Cogn Affect Neurosci 7: 623–634. doi: 10.1093/scan/nsr061
[13]	Lee JH, Kim J, Yoo SS (2012) Real-time fMRI-based neurofeedback reinforces causality of attention networks. Neurosci Res 72: 347–354. doi: 10.1016/j.neures.2012.01.002
[14]	Mccaig RG, Dixon M, Keramatian K, et al. (2011) Improved modulation of rostrolateral prefrontal cortex using real-time fMRI training and meta-cognitive awareness. Neuroimage 55: 1298–1305. doi: 10.1016/j.neuroimage.2010.12.016
[15]	Zhang G, Yao L, Zhang H, et al. (2013) Improved working memory performance through self-regulation of dorsal lateral prefrontal cortex activation using real-time fMRI. PLoS One 8: e73735. doi: 10.1371/journal.pone.0073735
[16]	Sherwood MS, Kane JH, Weisend MP, et al. (2016) Enhanced control of dorsolateral prefrontal cortex neurophysiology with real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback training and working memory practice. Neuroimage 124: 214–223. doi: 10.1016/j.neuroimage.2015.08.074
[17]	Sherwood MS, Weisend MP, Kane JH, et al. (2016) Combining real-time fMRI neurofeedback training of the DLPFC with N-Back practice results in neuroplastic effects confined to the neurofeedback target region. Front Behav Neurosci 10: 1–9.
[18]	Sitaram R, Veit R, Stevens B, 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
[19]	Berman BD, Horovitz SG, Venkataraman G, et al. (2012) Self-modulation of primary motor cortex activity with motor and motor imagery tasks using real-time fMRI-based neurofeedback. Neuroimage 59: 917–925. doi: 10.1016/j.neuroimage.2011.07.035
[20]	Chiew M, Laconte SM, Graham SJ (2012) Investigation of fMRI neurofeedback of differential primary motor cortex activity using kinesthetic motor imagery. Neuroimage 61: 21–31. doi: 10.1016/j.neuroimage.2012.02.053
[21]	Haller S, Birbaumer N, Veit R (2010) Real-time fMRI feedback training may improve chronic tinnitus. Eur Radiol 20: 696–703. doi: 10.1007/s00330-009-1595-z
[22]	Haller S, Kopel R, Jhooti P, et al. (2013) Dynamic reconfiguration of human brain functional networks through neurofeedback. Neuroimage 81: 243–252. doi: 10.1016/j.neuroimage.2013.05.019
[23]	Yoo SS, O'Leary HM, Fairneny T, et al. (2006) Increasing cortical activity in auditory areas through neurofeedback functional magnetic resonance imaging. Neuroreport 17: 1273–1278. doi: 10.1097/01.wnr.0000227996.53540.22
[24]	Johnston S, Linden DE, Healy D, et al. (2011) Upregulation of emotion areas through neurofeedback with a focus on positive mood. Cogn it Affective Behav Neurosci 11: 44–51. doi: 10.3758/s13415-010-0010-1
[25]	Johnston SJ, Boehm SG, Healy D, et al. (2010) Neurofeedback: A promising tool for the self-regulation of emotion networks. Neuroimage 49: 1066–1072. doi: 10.1016/j.neuroimage.2009.07.056
[26]	Rota G, Sitaram R, Veit R, et al. (2010) Self-regulation of regional cortical activity using real-time fMRI: The right inferior frontal gyrus and linguistic processing. Hum Brain Mapp 30: 1605–1614.
[27]	Scharnowski F, Hutton C, Josephs O, et al. (2012) Improving visual perception through neurofeedback. J Neurosci 32: 17830–17841. doi: 10.1523/JNEUROSCI.6334-11.2012
[28]	Shibata K, Kawato M (2011) Perceptual learning incepted by decoded fmri neurofeedback without stimulus presentation. Science 334: 1413–1415. doi: 10.1126/science.1212003
[29]	Decharms RC, Maeda F, Glover GH, 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
[30]	Ruiz S, Lee S, Soekadar SR, 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
[31]	Subramanian L, Hindle JV, Johnston S, et al. (2011) Real-time functional magnetic resonance imaging neurofeedback for treatment of Parkinson's disease. J Neurosci 31: 16309–16317. doi: 10.1523/JNEUROSCI.3498-11.2011
[32]	Linden DEJ, Habes I, Johnston SJ, et al. (2012) Real-time self-regulation of emotion networks in patients with depression. PLoS One 7: e38115. doi: 10.1371/journal.pone.0038115
[33]	Decharms RC, Christoff K, Glover GH, et al. (2004) Learned regulation of spatially localized brain activation using real-time fMRI. Neuroimage 21: 436–443. doi: 10.1016/j.neuroimage.2003.08.041
[34]	Yoo SS, Lee JH, O'Leary H, et al. (2008) Neurofeedback fMRI-mediated learning and consolidation of regional brain activation during motor imagery. Int J Imaging Syst Technol 18: 69–78. doi: 10.1002/ima.20139
[35]	Birbaumer N, Cohen LG (2007) Brain-computer interfaces: Communication and restoration of movement in paralysis. J Physiol 579: 621–636. doi: 10.1113/jphysiol.2006.125633
[36]	Daly JJ, Wolpaw JR (2008) Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 7: 1032–1043. doi: 10.1016/S1474-4422(08)70223-0
[37]	Ros T, Munneke MAM, Ruge D, et al. (2010) Endogenous control of waking brain rhythms induces neuroplasticity in humans. Eur J Neurosci 31: 770–778. doi: 10.1111/j.1460-9568.2010.07100.x
[38]	Orlov ND, Giampietro V, O'Daly O, et al. (2018) Real-time fMRI neurofeedback to down-regulate superior temporal gyrus activity in patients with schizophrenia and auditory hallucinations: A proof-of-concept study. Transl Psychiatry 8: 46. doi: 10.1038/s41398-017-0067-5
[39]	Paret C, Kluetsch R, Ruf M, et al. (2014) Down-regulation of amygdala activation with real-time fMRI neurofeedback in a healthy female sample. Front Behav Neurosci 8: 299.
[40]	Saliba J, Al-Reefi M, Carriere JS, et al. (2016) Accuracy of mobile-based audiometry in the evaluation of hearing loss in quiet and noisy environments. Otolaryngol Neck Surg 156: 706–711.
[41]	Thompson GP, Sladen DP, Borst BJ, et al. (2015) Accuracy of a tablet audiometer for measuring behavioral hearing thresholds in a clinical population. Otolaryngol Neck Surg 153: 838–842. doi: 10.1177/0194599815593737
[42]	Worsley KJ, Friston KJ (1995) Analysis of fMRI time-series revisited-again. Neuroimage 2: 173–181. doi: 10.1006/nimg.1995.1023
[43]	Ashby F Gregory (2011) Statistical analysis of fMRI data. MIT press.
[44]	Smith SM, Jenkinson M, Woolrich MW, et al. (2004) Advances in functional and structural MR image analysis and implementation as FSL. Math Brain Imaging 23: S208–S219.
[45]	Woolrich MW, Jbabdi S, Patenaude B, et al. (2009) Bayesian analysis of neuroimaging data in FSL. Math Brain Imaging 45: S173–S186.
[46]	Jenkinson M, Bannister P, Brady M, et al. (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
[47]	Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17: 143–155. doi: 10.1002/hbm.10062
[48]	Greve DN, Fischl B (2009) Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48: 63–72. doi: 10.1016/j.neuroimage.2009.06.060
[49]	Collins DL, Holmes CJ, Peters TM, et al. (2004) Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Mapp 3: 190–208.
[50]	Mazziotta J, Toga A, Evans A, et al. (2002) A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Brain Mapp Methods 356: 1293–1322.
[51]	Young KD, Zotev V, Phillips R, et al. (2014) Real-time fMRI neurofeedback training of amygdala activity in patients with major depressive disorder. PLoS One 9: e88785. doi: 10.1371/journal.pone.0088785
[52]	Sulzer J, Haller S, Scharnowski F, et al. (2013) Real-time fMRI neurofeedback: Progress and challenges. Neuroimage 76: 386–399. doi: 10.1016/j.neuroimage.2013.03.033
[53]	Cox RW, Jesmanowicz A, Hyde JS (1995) Real-time functional magnetic resonance imaging. Magn Reson Med 33: 230–236. doi: 10.1002/mrm.1910330213
[54]	Weiskopf N, Sitaram R, Josephs O, et al. (2007) Real-time functional magnetic resonance imaging: Methods and applications. Proc Int Sch Magn Reson Brain Funct 25: 989–1003.
[55]	Folmer RL, Griest S, Martin W (2001) Chronic tinnitus as phantom auditory pain. Otolaryngol-Head Neck Surg 124: 394–400. doi: 10.1067/mhn.2001.114673
[56]	Berliner KI, Shelton C, Hitselberger WE, et al. (1992) Acoustic tumors: Effect of surgical removal on tinnitus. Otol Neurotol 13: 13.
[57]	Gu JW, Halpin CF, Nam EC, et al. (2010) Tinnitus, diminished sound-level tolerance, and elevated auditory activity in humans with clinically normal hearing sensitivity. J Neurophysiol 104: 3361–3370. doi: 10.1152/jn.00226.2010
[58]	Seydellgreenwald A, Leaver AM, Turesky TK, et al. (2012) Functional MRI evidence for a role of ventral prefrontal cortex in tinnitus. Brain Ress 1485: 22–39. doi: 10.1016/j.brainres.2012.08.052
[59]	Wang H, Tian J, Yin D, et al. (2001) Regional glucose metabolic increases in left auditory cortex in tinnitus patients: A preliminary study with positron emission tomography. Chin Med J 114: 848–851.
[60]	Langguth B, Eichhammer P, Kreutzer A, et al. (2006) The impact of auditory cortex activity on characterizing and treating patients with chronic tinnitus-first results from a PET study. Acta Otolaryngol 126: 84–88. doi: 10.1080/03655230600895317
[61]	Schecklmann M, Landgrebe M, Poeppl TB, et al. (2013) Neural correlates of tinnitus duration and Distress: A positron emission tomography study. Hum Brain Mapp 34: 233–240. doi: 10.1002/hbm.21426
[62]	Geven LI, de Kleine E, Willemsen ATM, et al. (2014) Asymmetry in primary auditory cortex activity in tinnitus patients and controls. Neuroscience 256: 117–125. doi: 10.1016/j.neuroscience.2013.10.015
[63]	Kim SG, Ogawa S (2012) Biophysical and physiological origins of blood oxygenation level-dependent fMRI signals. J Cereb Blood Flow Metab 32: 1188–1206. doi: 10.1038/jcbfm.2012.23

Reader Comments

Your name:*

Email:*
© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)