Abstract

Real-time imaging of human brain has become an important technique within neuroimaging. In this study, a fast and efficient sensitivity map generation based on Finite Element Models (FEM) is developed which utilises a reduced sensitivitys matrix taking advantage of sparsity and parallelisation processes. Time and memory efficiency of these processes are evaluated and compared with conventional method showing that for a range of mesh densities from 50000 to 320000 nodes, the required memory is reduced over tenfold and computational time fourfold allowing for near real-time image recovery.

© 2015 Optical Society of America

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2015 (1)

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

2014 (3)

2013 (2)

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref] [PubMed]

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

2012 (4)

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

2011 (3)

M. Schweiger, “GPU-accelerated finite element method for modeling light transport in diffuse optical tomography,” Int. J. Biomed. Imaging 2011, 403892 (2011).
[Crossref] [PubMed]

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
[Crossref] [PubMed]

R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
[Crossref] [PubMed]

2009 (4)

S. Gupta, P. K. Yalavarthy, D. Roy, D. Piao, and R. M. Vasu, “Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography,” Med. Phys. 36(12), 5559–5567 (2009).
[Crossref] [PubMed]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Y. Lu and A. F. Chatziioannou, “A Parallel Adaptive Finite Element Method for the Simulation of Photon Migration with the Radiative-Transfer-Based Model,” Commun. Numer. Methods Eng. 25(6), 751–770 (2009).
[Crossref] [PubMed]

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

2008 (2)

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
[Crossref] [PubMed]

2007 (6)

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

S. M. LaConte, S. J. Peltier, and X. P. P. Hu, “Real-time fMRI using brain-state classification,” Hum. Brain Mapp. 28(10), 1033–1044 (2007).
[Crossref] [PubMed]

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

M. E. Eames, B. W. Pogue, P. K. Yalavarthy, and H. Dehghani, “An efficient Jacobian reduction method for diffuse optical image reconstruction,” Opt. Express 15(24), 15908–15919 (2007).
[Crossref] [PubMed]

T. Singer, “The neuronal basis of empathy and fairness,” Novartis Found. Symp. 278, 20–40 (2007).
[Crossref] [PubMed]

M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
[Crossref]

2006 (3)

M. Beauregard and J. Lévesque, “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,” Appl. Psychophysiol. Biofeedback 31(1), 3–20 (2006).
[Crossref] [PubMed]

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
[Crossref]

P. K. Yalavarthy, H. Dehghani, B. W. Pogue, and K. D. Paulsen, “Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis,” Opt. Express 14(13), 6113–6127 (2006).
[Crossref] [PubMed]

2005 (1)

H. Walter, B. Abler, A. Ciaramidaro, and S. Erk, “Motivating forces of human actions,” Brain Res. Bull. 67(5), 368–381 (2005).
[Crossref] [PubMed]

2004 (4)

R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
[Crossref] [PubMed]

S. G. Mason, R. Bohringer, J. F. Borisoff, and G. E. Birch, “Real-time control of a video game with a direct brain--computer interface,” J. Clin. Neurophysiol. 21(6), 404–408 (2004).
[Crossref] [PubMed]

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

A. Joshi, W. Bangerth, and E. Sevick-Muraca, “Adaptive finite element based tomography for fluorescence optical imaging in tissue,” Opt. Express 12(22), 5402–5417 (2004).
[Crossref] [PubMed]

2003 (2)

A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science 300(5626), 1755–1758 (2003).
[Crossref] [PubMed]

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
[Crossref] [PubMed]

2002 (1)

A. D. Craig, “How do you feel? Interoception: the sense of the physiological condition of the body,” Nat. Rev. Neurosci. 3(8), 655–666 (2002).
[Crossref] [PubMed]

2000 (1)

S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magn. Reson. Med. 44(3), 457–465 (2000).
[Crossref] [PubMed]

1999 (2)

S. Arridge and M. Schweiger, “Gradient-based optimisation scheme for optical tomography,” IEEE Trans. Med. Imaging 18, 262–271 (1999).
[Crossref] [PubMed]

A. H. Hielscher, A. D. Klose, and K. M. Hanson, “Gradient-based iterative image reconstruction scheme for time-resolved optical tomography,” IEEE Trans. Med. Imaging 18(3), 262–271 (1999).
[Crossref] [PubMed]

1996 (1)

1995 (2)

K. D. Paulsen and H. Jiang, “Spatially Varying Optical Property Reconstruction Using a Finite Element Diffusion Equation Approximation,” Med. Phys. 22(6), 691–701 (1995).
[Crossref] [PubMed]

S. R. Arridge and M. Schweiger, “Photon-measurement density functions. Part 2: Finite-element-method calculations,” Appl. Opt. 34(34), 8026–8037 (1995).
[Crossref] [PubMed]

Abler, B.

H. Walter, B. Abler, A. Ciaramidaro, and S. Erk, “Motivating forces of human actions,” Brain Res. Bull. 67(5), 368–381 (2005).
[Crossref] [PubMed]

Alerstam, E.

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

Andersson-Engels, S.

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
[Crossref] [PubMed]

Aronson, J. A.

A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science 300(5626), 1755–1758 (2003).
[Crossref] [PubMed]

Arridge, S.

S. Arridge and M. Schweiger, “Gradient-based optimisation scheme for optical tomography,” IEEE Trans. Med. Imaging 18, 262–271 (1999).
[Crossref] [PubMed]

Arridge, S. R.

S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
[Crossref]

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
[Crossref]

S. R. Arridge and M. Schweiger, “Photon-measurement density functions. Part 2: Finite-element-method calculations,” Appl. Opt. 34(34), 8026–8037 (1995).
[Crossref] [PubMed]

Bangerth, W.

Beauregard, M.

M. Beauregard and J. Lévesque, “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,” Appl. Psychophysiol. Biofeedback 31(1), 3–20 (2006).
[Crossref] [PubMed]

Birbaumer, N.

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
[Crossref] [PubMed]

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
[Crossref] [PubMed]

Birch, G. E.

S. G. Mason, R. Bohringer, J. F. Borisoff, and G. E. Birch, “Real-time control of a video game with a direct brain--computer interface,” J. Clin. Neurophysiol. 21(6), 404–408 (2004).
[Crossref] [PubMed]

Blankertz, B.

K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
[Crossref] [PubMed]

Blott, B. H.

M. Molinari, S. J. Cox, B. H. Blott, and G. J. Daniell, “Efficient non-linear 3D electrical tomography reconstruction,” Proceedings of the 2nd World Congress on Industrial Process Tomography424–432 (2001).

Bock, S. W.

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

Boehm, S. G.

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
[Crossref] [PubMed]

Bohringer, R.

S. G. Mason, R. Bohringer, J. F. Borisoff, and G. E. Birch, “Real-time control of a video game with a direct brain--computer interface,” J. Clin. Neurophysiol. 21(6), 404–408 (2004).
[Crossref] [PubMed]

Borisoff, J. F.

S. G. Mason, R. Bohringer, J. F. Borisoff, and G. E. Birch, “Real-time control of a video game with a direct brain--computer interface,” J. Clin. Neurophysiol. 21(6), 404–408 (2004).
[Crossref] [PubMed]

Caria, A.

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

Carpenter, C. M.

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
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Chatziioannou, A. F.

Y. Lu and A. F. Chatziioannou, “A Parallel Adaptive Finite Element Method for the Simulation of Photon Migration with the Radiative-Transfer-Based Model,” Commun. Numer. Methods Eng. 25(6), 751–770 (2009).
[Crossref] [PubMed]

Chen, C.

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Chen, J.-S.

J.-J. Tsai, N.-J. Chen, W.-C. Fang, and J.-S. Chen, “Fast Image Reconstruction Algorithm For Continuous Wave Diffuse Optical Tomography,” IEEE/NIH Life Science Systems and Applications Workshop93(2011).
[Crossref]

Chen, N.-J.

J.-J. Tsai, N.-J. Chen, W.-C. Fang, and J.-S. Chen, “Fast Image Reconstruction Algorithm For Continuous Wave Diffuse Optical Tomography,” IEEE/NIH Life Science Systems and Applications Workshop93(2011).
[Crossref]

Chen, W.

Christoff, K.

R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
[Crossref] [PubMed]

Ciaramidaro, A.

H. Walter, B. Abler, A. Ciaramidaro, and S. Erk, “Motivating forces of human actions,” Brain Res. Bull. 67(5), 368–381 (2005).
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Cohen, J. D.

A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science 300(5626), 1755–1758 (2003).
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Cox, S. J.

M. Molinari, S. J. Cox, B. H. Blott, and G. J. Daniell, “Efficient non-linear 3D electrical tomography reconstruction,” Proceedings of the 2nd World Congress on Industrial Process Tomography424–432 (2001).

Coyle, S. M.

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
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Craig, A. D.

A. D. Craig, “How do you feel? Interoception: the sense of the physiological condition of the body,” Nat. Rev. Neurosci. 3(8), 655–666 (2002).
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Culver, J. P.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
[Crossref] [PubMed]

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Curio, G.

K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
[Crossref] [PubMed]

Daniell, G. J.

M. Molinari, S. J. Cox, B. H. Blott, and G. J. Daniell, “Efficient non-linear 3D electrical tomography reconstruction,” Proceedings of the 2nd World Congress on Industrial Process Tomography424–432 (2001).

Davis, S. C.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref] [PubMed]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

deCharms, R. C.

R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
[Crossref] [PubMed]

Dehghani, H.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

M. E. Eames, B. W. Pogue, P. K. Yalavarthy, and H. Dehghani, “An efficient Jacobian reduction method for diffuse optical image reconstruction,” Opt. Express 15(24), 15908–15919 (2007).
[Crossref] [PubMed]

P. K. Yalavarthy, H. Dehghani, B. W. Pogue, and K. D. Paulsen, “Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis,” Opt. Express 14(13), 6113–6127 (2006).
[Crossref] [PubMed]

Dornhege, G.

K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
[Crossref] [PubMed]

Eames, M. E.

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

M. E. Eames, B. W. Pogue, P. K. Yalavarthy, and H. Dehghani, “An efficient Jacobian reduction method for diffuse optical image reconstruction,” Opt. Express 15(24), 15908–15919 (2007).
[Crossref] [PubMed]

Eggebrecht, A. T.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Erb, M.

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
[Crossref] [PubMed]

Erk, S.

H. Walter, B. Abler, A. Ciaramidaro, and S. Erk, “Motivating forces of human actions,” Brain Res. Bull. 67(5), 368–381 (2005).
[Crossref] [PubMed]

Fang, W.-C.

J.-J. Tsai, N.-J. Chen, W.-C. Fang, and J.-S. Chen, “Fast Image Reconstruction Algorithm For Continuous Wave Diffuse Optical Tomography,” IEEE/NIH Life Science Systems and Applications Workshop93(2011).
[Crossref]

Ferradal, S. L.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

X. Wu, A. T. Eggebrecht, S. L. Ferradal, J. P. Culver, and H. Dehghani, “Quantitative evaluation of atlas-based high-density diffuse optical tomography for imaging of the human visual cortex,” Biomed. Opt. Express 5(11), 3882–3900 (2014).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
[Crossref] [PubMed]

Gabrieli, J. D.

R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
[Crossref] [PubMed]

Gao, F.

Gerloff, C.

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

Ghadyani, H.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref] [PubMed]

Giladi, E.

M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
[Crossref]

Glover, G. H.

R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
[Crossref] [PubMed]

Goebel, R.

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
[Crossref] [PubMed]

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
[Crossref] [PubMed]

Gregg, N.

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
[Crossref] [PubMed]

Grodd, W.

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
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Gupta, S.

S. Gupta, P. K. Yalavarthy, D. Roy, D. Piao, and R. M. Vasu, “Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography,” Med. Phys. 36(12), 5559–5567 (2009).
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Guven, M.

M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
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Habes, I.

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
[Crossref] [PubMed]

Hanson, K. M.

A. H. Hielscher, A. D. Klose, and K. M. Hanson, “Gradient-based iterative image reconstruction scheme for time-resolved optical tomography,” IEEE Trans. Med. Imaging 18(3), 262–271 (1999).
[Crossref] [PubMed]

Hassanpour, M. S.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Healy, D.

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
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S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magn. Reson. Med. 44(3), 457–465 (2000).
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Hershey, T.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

Hielscher, A. H.

A. H. Hielscher, A. D. Klose, and K. M. Hanson, “Gradient-based iterative image reconstruction scheme for time-resolved optical tomography,” IEEE Trans. Med. Imaging 18(3), 262–271 (1999).
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Hu, X. P. P.

S. M. LaConte, S. J. Peltier, and X. P. P. Hu, “Real-time fMRI using brain-state classification,” Hum. Brain Mapp. 28(10), 1033–1044 (2007).
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Hummel, F.

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

Inder, T. E.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
[Crossref] [PubMed]

Intes, X.

M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
[Crossref]

Jermyn, M.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
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K. D. Paulsen and H. Jiang, “Enhanced frequency-domain optical image reconstruction in tissues through total-variation minimization,” Appl. Opt. 35(19), 3447–3458 (1996).
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Johnston, S.

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
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Joshi, A.

Kaipio, J. P.

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
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Kircher, T.

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

Klose, A. D.

A. H. Hielscher, A. D. Klose, and K. M. Hanson, “Gradient-based iterative image reconstruction scheme for time-resolved optical tomography,” IEEE Trans. Med. Imaging 18(3), 262–271 (1999).
[Crossref] [PubMed]

Kolehmainen, V.

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
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K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
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M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
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S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
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R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
[Crossref] [PubMed]

Lévesque, J.

M. Beauregard and J. Lévesque, “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,” Appl. Psychophysiol. Biofeedback 31(1), 3–20 (2006).
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Liao, S. M.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
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S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
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Y. Lu and A. F. Chatziioannou, “A Parallel Adaptive Finite Element Method for the Simulation of Photon Migration with the Radiative-Transfer-Based Model,” Commun. Numer. Methods Eng. 25(6), 751–770 (2009).
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S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
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Mathiak, K.

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
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N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
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S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magn. Reson. Med. 44(3), 457–465 (2000).
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Müller, K. R.

K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
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A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science 300(5626), 1755–1758 (2003).
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P. K. Yalavarthy, H. Dehghani, B. W. Pogue, and K. D. Paulsen, “Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis,” Opt. Express 14(13), 6113–6127 (2006).
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R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
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Peltier, S. J.

S. M. LaConte, S. J. Peltier, and X. P. P. Hu, “Real-time fMRI using brain-state classification,” Hum. Brain Mapp. 28(10), 1033–1044 (2007).
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Piao, D.

S. Gupta, P. K. Yalavarthy, D. Roy, D. Piao, and R. M. Vasu, “Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography,” Med. Phys. 36(12), 5559–5567 (2009).
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M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
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H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
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M. E. Eames, B. W. Pogue, P. K. Yalavarthy, and H. Dehghani, “An efficient Jacobian reduction method for diffuse optical image reconstruction,” Opt. Express 15(24), 15908–15919 (2007).
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P. K. Yalavarthy, H. Dehghani, B. W. Pogue, and K. D. Paulsen, “Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis,” Opt. Express 14(13), 6113–6127 (2006).
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Rana, M.

R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
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Rilling, J. K.

A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science 300(5626), 1755–1758 (2003).
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Robichaux-Viehoever, A.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
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Roy, D.

S. Gupta, P. K. Yalavarthy, D. Roy, D. Piao, and R. M. Vasu, “Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography,” Med. Phys. 36(12), 5559–5567 (2009).
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S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
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R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
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Sanfey, A. G.

A. G. Sanfey, J. K. Rilling, J. A. Aronson, L. E. Nystrom, and J. D. Cohen, “The neural basis of economic decision-making in the Ultimatum Game,” Science 300(5626), 1755–1758 (2003).
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Schad, L. R.

S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magn. Reson. Med. 44(3), 457–465 (2000).
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Scharnowski, F.

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
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Schlaggar, B. L.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
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Shimony, J. S.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

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T. Singer, “The neuronal basis of empathy and fairness,” Novartis Found. Symp. 278, 20–40 (2007).
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Sitaram, R.

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
[Crossref] [PubMed]

Smyser, C. D.

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

Snyder, A. Z.

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Soekadar, S. R.

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

Somersalo, E.

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
[Crossref]

Srinivasan, S.

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

Stevens, B.

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
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Svensson, T.

E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
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Tangermann, M.

K. R. Müller, M. Tangermann, G. Dornhege, M. Krauledat, G. Curio, and B. Blankertz, “Machine learning for real-time single-trial EEG-analysis: from brain-computer interfacing to mental state monitoring,” J. Neurosci. Methods 167(1), 82–90 (2008).
[Crossref] [PubMed]

Tarvainen, T.

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
[Crossref]

Thesen, S.

S. Thesen, O. Heid, E. Mueller, and L. R. Schad, “Prospective acquisition correction for head motion with image-based tracking for real-time fMRI,” Magn. Reson. Med. 44(3), 457–465 (2000).
[Crossref] [PubMed]

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J.-J. Tsai, N.-J. Chen, W.-C. Fang, and J.-S. Chen, “Fast Image Reconstruction Algorithm For Continuous Wave Diffuse Optical Tomography,” IEEE/NIH Life Science Systems and Applications Workshop93(2011).
[Crossref]

Turner, W.

M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
[Crossref] [PubMed]

Vasu, R. M.

S. Gupta, P. K. Yalavarthy, D. Roy, D. Piao, and R. M. Vasu, “Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography,” Med. Phys. 36(12), 5559–5567 (2009).
[Crossref] [PubMed]

Vauhkonen, M.

S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
[Crossref]

Veit, R.

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
[Crossref] [PubMed]

R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
[Crossref] [PubMed]

R. Sitaram, S. Lee, S. Ruiz, M. Rana, R. Veit, and N. Birbaumer, “Real-time support vector classification and feedback of multiple emotional brain states,” Neuroimage 56(2), 753–765 (2011).
[Crossref] [PubMed]

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
[Crossref] [PubMed]

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H. Walter, B. Abler, A. Ciaramidaro, and S. Erk, “Motivating forces of human actions,” Brain Res. Bull. 67(5), 368–381 (2005).
[Crossref] [PubMed]

Wan, W.

Wang, X.

Ward, T. E.

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
[Crossref] [PubMed]

Weiskopf, N.

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
[Crossref] [PubMed]

N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
[Crossref] [PubMed]

White, B. R.

S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
[Crossref] [PubMed]

Whitfield, S.

R. C. deCharms, K. Christoff, G. H. Glover, J. M. Pauly, S. Whitfield, and J. D. Gabrieli, “Learned regulation of spatially localized brain activation using real-time fMRI,” Neuroimage 21(1), 436–443 (2004).
[Crossref] [PubMed]

Wu, X.

Yalavarthy, P. K.

H. Dehghani, M. E. Eames, P. K. Yalavarthy, S. C. Davis, S. Srinivasan, C. M. Carpenter, B. W. Pogue, and K. D. Paulsen, “Near infrared optical tomography using NIRFAST: Algorithm for numerical model and image reconstruction,” Commun. Numer. Methods Eng. 25(6), 711–732 (2009).
[Crossref] [PubMed]

S. Gupta, P. K. Yalavarthy, D. Roy, D. Piao, and R. M. Vasu, “Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography,” Med. Phys. 36(12), 5559–5567 (2009).
[Crossref] [PubMed]

M. E. Eames, B. W. Pogue, P. K. Yalavarthy, and H. Dehghani, “An efficient Jacobian reduction method for diffuse optical image reconstruction,” Opt. Express 15(24), 15908–15919 (2007).
[Crossref] [PubMed]

P. K. Yalavarthy, H. Dehghani, B. W. Pogue, and K. D. Paulsen, “Critical computational aspects of near infrared circular tomographic imaging: Analysis of measurement number, mesh resolution and reconstruction basis,” Opt. Express 14(13), 6113–6127 (2006).
[Crossref] [PubMed]

Yazici, B.

M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
[Crossref]

Yi, X.

Zeff, B. W.

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
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Zhan, Y.

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
[Crossref] [PubMed]

A. T. Eggebrecht, B. R. White, S. L. Ferradal, C. Chen, Y. Zhan, A. Z. Snyder, H. Dehghani, and J. P. Culver, “A quantitative spatial comparison of high-density diffuse optical tomography and fMRI cortical mapping,” Neuroimage 61(4), 1120–1128 (2012).
[Crossref] [PubMed]

Zhao, H.

Appl. Opt. (3)

Appl. Psychophysiol. Biofeedback (1)

M. Beauregard and J. Lévesque, “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,” Appl. Psychophysiol. Biofeedback 31(1), 3–20 (2006).
[Crossref] [PubMed]

Biomed. Opt. Express (1)

Brain Res. Bull. (1)

H. Walter, B. Abler, A. Ciaramidaro, and S. Erk, “Motivating forces of human actions,” Brain Res. Bull. 67(5), 368–381 (2005).
[Crossref] [PubMed]

Cereb. Cortex (1)

S. L. Ferradal, S. M. Liao, A. T. Eggebrecht, J. S. Shimony, T. E. Inder, J. P. Culver, and C. D. Smyser, “Functional imaging of the developing brain at the bedside using diffuse optical tomography,” Cereb. Cortex 93, 320 (2015).
[PubMed]

Cogn. Affect. Behav. Neurosci. (1)

S. Johnston, D. E. J. Linden, D. Healy, R. Goebel, I. Habes, and S. G. Boehm, “Upregulation of emotion areas through neurofeedback with a focus on positive mood,” Cogn. Affect. Behav. Neurosci. 11(1), 44–51 (2011).
[Crossref] [PubMed]

Commun. Numer. Methods Eng. (2)

Y. Lu and A. F. Chatziioannou, “A Parallel Adaptive Finite Element Method for the Simulation of Photon Migration with the Radiative-Transfer-Based Model,” Commun. Numer. Methods Eng. 25(6), 751–770 (2009).
[Crossref] [PubMed]

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Front. Neuroenergetics (1)

Y. Zhan, A. T. Eggebrecht, J. P. Culver, and H. Dehghani, “Image quality analysis of high-density diffuse optical tomography incorporating a subject-specific head model,” Front. Neuroenergetics 4, 6 (2012).
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Hum. Brain Mapp. (2)

S. Ruiz, S. Lee, S. R. Soekadar, A. Caria, R. Veit, T. Kircher, N. Birbaumer, and R. Sitaram, “Acquired self-control of insula cortex modulates emotion recognition and brain network connectivity in schizophrenia,” Hum. Brain Mapp. 34(1), 200–212 (2013).
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S. M. LaConte, S. J. Peltier, and X. P. P. Hu, “Real-time fMRI using brain-state classification,” Hum. Brain Mapp. 28(10), 1033–1044 (2007).
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IEEE Trans. Biomed. Eng. (1)

N. Weiskopf, K. Mathiak, S. W. Bock, F. Scharnowski, R. Veit, W. Grodd, R. Goebel, and N. Birbaumer, “Principles of a brain-computer interface (BCI) based on real-time functional magnetic resonance imaging (fMRI),” IEEE Trans. Biomed. Eng. 51(6), 966–970 (2004).
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M. Schweiger, “GPU-accelerated finite element method for modeling light transport in diffuse optical tomography,” Int. J. Biomed. Imaging 2011, 403892 (2011).
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M. Guven, B. Yazici, K. Kwon, E. Giladi, and X. Intes, “Effect of discretization error and adaptive mesh generation in diffuse optical absorption imaging,” Inverse Probl. 23(3), 1135–1160 (2007).
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S. R. Arridge and J. C. Schotland, “Optical tomography: forward and inverse problems,” Inverse Probl. 25(12), 123010 (2009).
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S. R. Arridge, J. P. Kaipio, V. Kolehmainen, M. Schweiger, E. Somersalo, T. Tarvainen, and M. Vauhkonen, “Approximation errors and model reduction with an application in optical diffusion tomography,” Inverse Probl. 22(1), 175–195 (2006).
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E. Alerstam, T. Svensson, and S. Andersson-Engels, “Parallel computing with graphics processing units for high-speed Monte Carlo simulation of photon migration,” J. Biomed. Opt. 13(6), 060504 (2008).
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M. Jermyn, H. Ghadyani, M. A. Mastanduno, W. Turner, S. C. Davis, H. Dehghani, and B. W. Pogue, “Fast segmentation and high-quality three-dimensional volume mesh creation from medical images for diffuse optical tomography,” J. Biomed. Opt. 18(8), 086007 (2013).
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S. M. Liao, S. L. Ferradal, B. R. White, N. Gregg, T. E. Inder, and J. P. Culver, “High-density diffuse optical tomography of term infant visual cortex in the nursery,” J. Biomed. Opt. 17(8), 081414 (2012).
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S. G. Mason, R. Bohringer, J. F. Borisoff, and G. E. Birch, “Real-time control of a video game with a direct brain--computer interface,” J. Clin. Neurophysiol. 21(6), 404–408 (2004).
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J. Neural Eng. (1)

S. M. Coyle, T. E. Ward, and C. M. Markham, “Brain-computer interface using a simplified functional near-infrared spectroscopy system,” J. Neural Eng. 4(3), 219–226 (2007).
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Magn. Reson. Med. (1)

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Nat. Photonics (1)

A. T. Eggebrecht, S. L. Ferradal, A. Robichaux-Viehoever, M. S. Hassanpour, H. Dehghani, A. Z. Snyder, T. Hershey, and J. P. Culver, “Mapping distributed brain function and networks with diffuse optical tomography,” Nat. Photonics 8(6), 448–454 (2014).
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N. Weiskopf, R. Veit, M. Erb, K. Mathiak, W. Grodd, R. Goebel, and N. Birbaumer, “Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data,” Neuroimage 19(3), 577–586 (2003).
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R. Sitaram, R. Veit, B. Stevens, A. Caria, C. Gerloff, N. Birbaumer, and F. Hummel, “Acquired Control of Ventral Premotor Cortex Activity by Feedback Training: An Exploratory Real-Time FMRI and TMS Study,” Neurorehabil. Neural Repair 26(3), 256–265 (2012).
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Proc. Natl. Acad. Sci. U.S.A. (1)

B. W. Zeff, B. R. White, H. Dehghani, B. L. Schlaggar, and J. P. Culver, “Retinotopic mapping of adult human visual cortex with high-density diffuse optical tomography,” Proc. Natl. Acad. Sci. U.S.A. 104(29), 12169–12174 (2007).
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Figures (11)

Fig. 1
Fig. 1 An example of the sensitivity calculated for a given source/detector pair on a full head model using conventional and approximated method. Each contour line represents 10 – 0.0001% of the maximum absolute sensitivity value.
Fig. 2
Fig. 2 High-density source and detector positions for the whole-head HD-DOT.
Fig. 3
Fig. 3 Accuracy of the sensitivity matrix as a function of FEM mesh nodal density with respect to ground truth (high density mesh). The accuracy is calculated on a node by node basis and error bars represent the variation across the whole model with the ROI.
Fig. 4
Fig. 4 An example of sensitivity error for reduced sparse sensitivity matrices with different thresholds as compared to conventional full matrix.
Fig. 5
Fig. 5 Accuracy evaluation of the reduced sparse sensitivity matrices with different thresholds as compared to conventional full matrix. The red line represents the 1% sensitivity threshold.
Fig. 6
Fig. 6 Recovery result of whole cortex.
Fig. 7
Fig. 7 Size of the sensitivity matrix for 8 meshes.
Fig. 8
Fig. 8 Reduction of size of the sensitivity matrix for 8 meshes.
Fig. 9
Fig. 9 Processing time of the light field generation.
Fig. 10
Fig. 10 Processing time of the sensitivity matrix generation only from calculated light fields and the reduction based on parallelisation and reduced sensitivity matrix.
Fig. 11
Fig. 11 Total processing time of sensitivity generation processes.

Tables (1)

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Table 1 Head tissue optical properties at 750 nm.

Equations (7)

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JΔμ=Δy
J=[ ln I 1 μ a 1 ln I 1 μ a 2 ... ln I 1 μ a NN ln I 2 μ a 1 ln I 2 μ a 2 ... ln I 2 μ a NN ... ... ... ... ln I NM μ a 1 ln I NM μ a 2 ... ln I NM μ a NN ]
Δμ= J ˜ T ( J ˜ J ˜ T +αI ) 1 Δy
J ˜ = J diag( diag( J T J )+β( max( diag( J T J ) ) ) )
J(j,i,r)=[ k| N k ϵτ( r ) Φ k i u k ( r ) ]×[ k| N k ϵτ( r ) Φ Adj,k j u k ( r ) ]
J(j,i,r)= Φ i (r)× Φ Adj j (r)
J err = | J subj J ref | J ref ×100%

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