Abstract

Functional near infrared spectroscopy (fNIRS) is a promising neuroimaging method for investigating networks of cortical regions over time. We propose a directed effective connectivity method (TPDC) allowing the capture of both time and frequency evolution of the brain’s networks using fNIRS data acquired from healthy subjects performing a continuous finger-tapping task. Using this method we show the directed connectivity patterns among cortical motor regions involved in the task and their significant variations in the strength of information flow exchanges. Intra and inter-hemispheric connections during the motor task with their temporal evolution are also provided. Characterisation of the fluctuations in brain connectivity opens up a new way to assess the organisation of the brain to adapt to changing task constraints, or under pathological conditions.

© 2017 Optical Society of America

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2017 (1)

V. Chiosa, S. A. Groppa, D. Ciolac, N. Koirala, L. Mişina, Y. Winter, M. Moldovanu, M. Muthuraman, and S. Groppa, “Breakdown of Thalamo-Cortical Connectivity Precedes Spike Generation in Focal Epilepsies,” Brain Connect. 7(5), 309–320 (2017).
[Crossref] [PubMed]

2016 (2)

A. R. Anwar, M. Muthalib, S. Perrey, A. Galka, O. Granert, S. Wolff, U. Heute, G. Deuschl, J. Raethjen, and M. Muthuraman, “Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study,” Brain Topogr. 29(5), 645–660 (2016).
[Crossref] [PubMed]

K. J. Blinowska, F. Rakowski, M. Kaminski, F. D. V. Fallani, C. Del Percio, R. Lizio, and C. Babiloni, “Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms,” Clin. Neurophysiol. 128, 667–680 (2016).
[PubMed]

2015 (3)

M. Muthuraman, G. Deuschl, A. R. Anwar, K. G. Mideksa, F. von Helmolt, and S. A. Schneider, “Essential and aging-related tremor: Differences of central control,” Mov. Disord. 30(12), 1673–1680 (2015).
[Crossref] [PubMed]

A. K. Seth, A. B. Barrett, and L. Barnett, “Granger causality analysis in neuroscience and neuroimaging,” J. Neurosci. 35(8), 3293–3297 (2015).
[Crossref] [PubMed]

W. H. Thompson and P. Fransson, “The frequency dimension of fMRI dynamic connectivity: Network connectivity, functional hubs and integration in the resting brain,” Neuroimage 121, 227–242 (2015).
[Crossref] [PubMed]

2014 (7)

F. De Vico Fallani, J. Richiardi, M. Chavez, and S. Achard, “Graph analysis of functional brain networks: practical issues in translational neuroscience,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 369(1653), 20130521 (2014).
[Crossref] [PubMed]

S. Bajaj, D. Drake, A. J. Butler, and M. Dhamala, “Oscillatory motor network activity during rest and movement: an fNIRS study,” Front. Syst. Neurosci. 8, 13 (2014).
[Crossref] [PubMed]

F. Scholkmann, S. Kleiser, A. J. Metz, R. Zimmermann, J. Mata Pavia, U. Wolf, and M. Wolf, “A review on continuous wave functional near-infrared spectroscopy and imaging instrumentation and methodology,” Neuroimage 85(Pt 1), 6–27 (2014).
[Crossref] [PubMed]

G. Derosière, F. Alexandre, N. Bourdillon, K. Mandrick, T. E. Ward, and S. Perrey, “Similar scaling of contralateral and ipsilateral cortical responses during graded unimanual force generation,” Neuroimage 85(Pt 1), 471–477 (2014).
[Crossref] [PubMed]

M. Muthuraman, H. Hellriegel, N. Hoogenboom, A. R. Anwar, K. G. Mideksa, H. Krause, A. Schnitzler, G. Deuschl, and J. Raethjen, “Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements,” PLoS One 9(3), e91441 (2014).
[Crossref] [PubMed]

L. Zhang, J. Sun, B. Sun, Q. Luo, and H. Gong, “Studying hemispheric lateralization during a Stroop task through near-infrared spectroscopy-based connectivity,” J. Biomed. Opt. 19(5), 057012 (2014).
[Crossref] [PubMed]

A. V. Medvedev, “Does the resting state connectivity have hemispheric asymmetry? A near-infrared spectroscopy study,” Neuroimage 85(Pt 1), 400–407 (2014).
[Crossref] [PubMed]

2013 (9)

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

M. Xia, J. Wang, and Y. He, “BrainNet Viewer: a network visualization tool for human brain connectomics,” PLoS One 8(7), e68910 (2013).
[Crossref] [PubMed]

X. Wen, G. Rangarajan, and M. Ding, “Is Granger causality a viable technique for analyzing fMRI data?” PLoS One 8(7), e67428 (2013).
[Crossref] [PubMed]

Z. Yuan, “Combining independent component analysis and Granger causality to investigate brain network dynamics with fNIRS measurements,” Biomed. Opt. Express 4(11), 2629–2643 (2013).
[Crossref] [PubMed]

B. Zhu and A. Godavarty, “Functional connectivity in the brain in joint attention skills using near infrared spectroscopy and imaging,” Behav. Brain Res. 250, 28–31 (2013).
[Crossref] [PubMed]

K. Friston, R. Moran, and A. K. Seth, “Analysing connectivity with Granger causality and dynamic causal modelling,” Curr. Opin. Neurobiol. 23(2), 172–178 (2013).
[Crossref] [PubMed]

H. Sato, N. Yahata, T. Funane, R. Takizawa, T. Katura, H. Atsumori, Y. Nishimura, A. Kinoshita, M. Kiguchi, H. Koizumi, M. Fukuda, and K. Kasai, “A NIRS-fMRI investigation of prefrontal cortex activity during a working memory task,” Neuroimage 83, 158–173 (2013).
[Crossref] [PubMed]

C. J. Stoodley and J. F. Stein, “Cerebellar function in developmental dyslexia,” Cerebellum 12(2), 267–276 (2013).
[Crossref] [PubMed]

O. Sporns, “Network attributes for segregation and integration in the human brain,” Curr. Opin. Neurobiol. 23(2), 162–171 (2013).
[Crossref] [PubMed]

2012 (10)

M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage 63(2), 921–935 (2012).
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D. Lehmann, P. L. Faber, S. Tei, R. D. Pascual-Marqui, P. Milz, and K. Kochi, “Reduced functional connectivity between cortical sources in five meditation traditions detected with lagged coherence using EEG tomography,” Neuroimage 60(2), 1574–1586 (2012).
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C. J. Price, “A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading,” Neuroimage 62(2), 816–847 (2012).
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O. Sporns, “From simple graphs to the connectome: networks in neuroimaging,” Neuroimage 62(2), 881–886 (2012).
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B. Roelstraete and Y. Rosseel, “Does partial Granger causality really eliminate the influence of exogenous inputs and latent variables?” J. Neurosci. Methods 206(1), 73–77 (2012).
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B. Molavi and G. A. Dumont, “Wavelet-based motion artifact removal for functional near-infrared spectroscopy,” Physiol. Meas. 33(2), 259–270 (2012).
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R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

O. Sporns, “From simple graphs to the connectome: networks in neuroimaging,” Neuroimage 62(2), 881–886 (2012).
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L. Holper, F. Scholkmann, and M. Wolf, “Between-brain connectivity during imitation measured by fNIRS,” Neuroimage 63(1), 212–222 (2012).
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M. Muthuraman, K. Arning, R. B. Govindan, U. Heute, G. Deuschl, and J. Raethjen, “Cortical representation of different motor rhythms during bimanual movements,” Exp. Brain Res. 223(4), 489–504 (2012).
[Crossref] [PubMed]

2011 (4)

D. R. Leff, F. Orihuela-Espina, C. E. Elwell, T. Athanasiou, D. T. Delpy, A. W. Darzi, and G. Z. Yang, “Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies,” Neuroimage 54(4), 2922–2936 (2011).
[Crossref] [PubMed]

L. Barnett and A. K. Seth, “Behaviour of Granger causality under filtering: theoretical invariance and practical application,” J. Neurosci. Methods 201(2), 404–419 (2011).
[Crossref] [PubMed]

C. H. Park, W. H. Chang, S. H. Ohn, S. T. Kim, O. Y. Bang, A. Pascual-Leone, and Y. H. Kim, “Longitudinal changes of resting-state functional connectivity during motor recovery after stroke,” Stroke 42(5), 1357–1362 (2011).
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C. Grefkes and G. R. Fink, “Reorganization of cerebral networks after stroke: new insights from neuroimaging with connectivity approaches,” Brain 134(5), 1264–1276 (2011).
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2010 (9)

K. E. Stephan and K. J. Friston, “Analyzing effective connectivity with functional magnetic resonance imaging,” Wiley Interdiscip. Rev. Cogn. Sci. 1(3), 446–459 (2010).
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M. P. van den Heuvel and H. E. Hulshoff Pol, “Exploring the brain network: a review on resting-state fMRI functional connectivity,” Eur. Neuropsychopharmacol. 20(8), 519–534 (2010).
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R. C. Mesquita, M. A. Franceschini, and D. A. Boas, “Resting state functional connectivity of the whole head with near-infrared spectroscopy,” Biomed. Opt. Express 1(1), 324–336 (2010).
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M. P. van Meer, K. van der Marel, K. Wang, W. M. Otte, S. El Bouazati, T. A. Roeling, M. A. Viergever, J. W. Berkelbach van der Sprenkel, and R. M. Dijkhuizen, “Recovery of sensorimotor function after experimental stroke correlates with restoration of resting-state interhemispheric functional connectivity,” J. Neurosci. 30(11), 3964–3972 (2010).
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A. K. Seth, “A MATLAB toolbox for Granger causal connectivity analysis,” J. Neurosci. Methods 186(2), 262–273 (2010).
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F. Scholkmann, S. Spichtig, T. Muehlemann, and M. Wolf, “How to detect and reduce movement artifacts in near-infrared imaging using moving standard deviation and spline interpolation,” Physiol. Meas. 31(5), 649–662 (2010).
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C. Chang and G. H. Glover, “Time-frequency dynamics of resting-state brain connectivity measured with fMRI,” Neuroimage 50(1), 81–98 (2010).
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C. H. Im, Y. J. Jung, S. Lee, D. Koh, D. W. Kim, and B. M. Kim, “Estimation of directional coupling between cortical areas using Near-Infrared Spectroscopy (NIRS),” Opt. Express 18(6), 5730–5739 (2010).
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V. Nedelko, T. Hassa, F. Hamzei, C. Weiller, F. Binkofski, M. A. Schoenfeld, O. Tüscher, and C. Dettmers, “Age-independent activation in areas of the mirror neuron system during action observation and action imagery. A fMRI study,” Restor. Neurol. Neurosci. 28(6), 737–747 (2010).
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2009 (3)

B. Schelter, J. Timmer, and M. Eichler, “Assessing the strength of directed influences among neural signals using renormalized partial directed coherence,” J. Neurosci. Methods 179(1), 121–130 (2009).
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K. J. Friston, “Modalities, modes, and models in functional neuroimaging,” Science 326(5951), 399–403 (2009).
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T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

2008 (5)

S. Halim and I. N. Bisono, “Automatic seasonal auto regressive moving average models and unit root test detection,” International Journal of Management Science and Engineering Management 3(4), 266–274 (2008).

D. J. Serrien, “Coordination constraints during bimanual versus unimanual performance conditions,” Neuropsychologia 46(2), 419–425 (2008).
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S. Guo, A. K. Seth, K. M. Kendrick, C. Zhou, and J. Feng, “Partial Granger causality--eliminating exogenous inputs and latent variables,” J. Neurosci. Methods 172(1), 79–93 (2008).
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J. Gervain, F. Macagno, S. Cogoi, M. Peña, and J. Mehler, “The neonate brain detects speech structure,” Proc. Natl. Acad. Sci. U.S.A. 105(37), 14222–14227 (2008).
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S. T. Witt, A. R. Laird, and M. E. Meyerand, “Functional neuroimaging correlates of finger-tapping task variations: an ALE meta-analysis,” Neuroimage 42(1), 343–356 (2008).
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2007 (2)

C. J. Stam, G. Nolte, and A. Daffertshofer, “Phase lag index: assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources,” Hum. Brain Mapp. 28(11), 1178–1193 (2007).
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G. Themelis, H. D’Arceuil, S. G. Diamond, S. Thaker, T. J. Huppert, D. A. Boas, and M. A. Franceschini, “Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations,” J. Biomed. Opt. 12(1), 014033 (2007).
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2006 (3)

B. Pollok, J. Gross, and A. Schnitzler, “How the brain controls repetitive finger movements,” J. Physiol. Paris 99(1), 8–13 (2006).
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L. Kocsis, P. Herman, and A. Eke, “The modified Beer-Lambert law revisited,” Phys. Med. Biol. 51(5), N91–N98 (2006).
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F. T. Sun, L. M. Miller, A. A. Rao, and M. D’Esposito, “Functional connectivity of cortical networks involved in bimanual motor sequence learning,” Cereb. Cortex 17(5), 1227–1234 (2006).
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2005 (1)

A. K. Singh, M. Okamoto, H. Dan, V. Jurcak, and I. Dan, “Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI,” Neuroimage 27(4), 842–851 (2005).
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2004 (2)

N. K. Logothetis and B. A. Wandell, “Interpreting the BOLD signal,” Annu. Rev. Physiol. 66(1), 735–769 (2004).
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W. D. Penny, K. E. Stephan, A. Mechelli, and K. J. Friston, “Comparing dynamic causal models,” Neuroimage 22(3), 1157–1172 (2004).
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2003 (2)

W. Hesse, E. Möller, M. Arnold, and B. Schack, “The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies,” J. Neurosci. Methods 124(1), 27–44 (2003).
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A. Korzeniewska, M. Mańczak, M. Kamiński, K. J. Blinowska, and S. Kasicki, “Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method,” J. Neurosci. Methods 125(1-2), 195–207 (2003).
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2002 (1)

J. C. Dreher and K. F. Berman, “Fractionating the neural substrate of cognitive control processes,” Proc. Natl. Acad. Sci. U.S.A. 99(22), 14595–14600 (2002).
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2001 (4)

T. Schneider and A. Neumaier, “Algorithm 808: ARfit—A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models,” ACM Trans. Math. Softw. 27(1), 58–65 (2001).
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L. A. Baccalá and K. Sameshima, “Partial directed coherence: a new concept in neural structure determination,” Biol. Cybern. 84(6), 463–474 (2001).
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M. Kamiński, M. Ding, W. A. Truccolo, and S. L. Bressler, “Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance,” Biol. Cybern. 85(2), 145–157 (2001).
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L. A. Baccalá and K. Sameshima, “Overcoming the limitations of correlation analysis for many simultaneously processed neural structures,” Prog. Brain Res. 130, 33–47 (2001).
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1999 (1)

K. Sameshima and L. A. Baccalá, “Using partial directed coherence to describe neuronal ensemble interactions,” J. Neurosci. Methods 94(1), 93–103 (1999).
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1996 (2)

A. Duncan, J. H. Meek, M. Clemence, C. E. Elwell, P. Fallon, L. Tyszczuk, M. Cope, and D. T. Delpy, “Measurement of cranial optical path length as a function of age using phase resolved near infrared spectroscopy,” Pediatr. Res. 39(5), 889–894 (1996).
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S. M. Rao, P. A. Bandettini, J. R. Binder, J. A. Bobholz, T. A. Hammeke, E. A. Stein, and J. S. Hyde, “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex,” J. Cereb. Blood Flow Metab. 16(6), 1250–1254 (1996).
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1995 (1)

B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med. 34(4), 537–541 (1995).
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1994 (1)

G. Tononi, O. Sporns, and G. M. Edelman, “A measure for brain complexity: relating functional segregation and integration in the nervous system,” Proc. Natl. Acad. Sci. U.S.A. 91(11), 5033–5037 (1994).
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1982 (1)

J. Geweke, “Measurement of linear dependence and feedback between multiple time series,” J. Am. Stat. Assoc. 77(378), 304–313 (1982).
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1973 (1)

A. M. Wing and A. B. Kristofferson, “Response delays and the timing of discrete motor responses,” Atten. Percept. Psychophys. 14(1), 5–12 (1973).
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1971 (1)

R. C. Oldfield, “The assessment and analysis of handedness: the Edinburgh inventory,” Neuropsychologia 9(1), 97–113 (1971).
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1969 (1)

C. W. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica 37(3), 424–438 (1969).
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Achard, S.

F. De Vico Fallani, J. Richiardi, M. Chavez, and S. Achard, “Graph analysis of functional brain networks: practical issues in translational neuroscience,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 369(1653), 20130521 (2014).
[Crossref] [PubMed]

Alexandre, F.

G. Derosière, F. Alexandre, N. Bourdillon, K. Mandrick, T. E. Ward, and S. Perrey, “Similar scaling of contralateral and ipsilateral cortical responses during graded unimanual force generation,” Neuroimage 85(Pt 1), 471–477 (2014).
[Crossref] [PubMed]

Allen, E. A.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

Anwar, A. R.

A. R. Anwar, M. Muthalib, S. Perrey, A. Galka, O. Granert, S. Wolff, U. Heute, G. Deuschl, J. Raethjen, and M. Muthuraman, “Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study,” Brain Topogr. 29(5), 645–660 (2016).
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M. Muthuraman, G. Deuschl, A. R. Anwar, K. G. Mideksa, F. von Helmolt, and S. A. Schneider, “Essential and aging-related tremor: Differences of central control,” Mov. Disord. 30(12), 1673–1680 (2015).
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M. Muthuraman, H. Hellriegel, N. Hoogenboom, A. R. Anwar, K. G. Mideksa, H. Krause, A. Schnitzler, G. Deuschl, and J. Raethjen, “Beamformer source analysis and connectivity on concurrent EEG and MEG data during voluntary movements,” PLoS One 9(3), e91441 (2014).
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Arning, K.

M. Muthuraman, K. Arning, R. B. Govindan, U. Heute, G. Deuschl, and J. Raethjen, “Cortical representation of different motor rhythms during bimanual movements,” Exp. Brain Res. 223(4), 489–504 (2012).
[Crossref] [PubMed]

Arnold, M.

W. Hesse, E. Möller, M. Arnold, and B. Schack, “The use of time-variant EEG Granger causality for inspecting directed interdependencies of neural assemblies,” J. Neurosci. Methods 124(1), 27–44 (2003).
[Crossref] [PubMed]

Ashina, M.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

Athanasiou, T.

D. R. Leff, F. Orihuela-Espina, C. E. Elwell, T. Athanasiou, D. T. Delpy, A. W. Darzi, and G. Z. Yang, “Assessment of the cerebral cortex during motor task behaviours in adults: a systematic review of functional near infrared spectroscopy (fNIRS) studies,” Neuroimage 54(4), 2922–2936 (2011).
[Crossref] [PubMed]

Atsumori, H.

H. Sato, N. Yahata, T. Funane, R. Takizawa, T. Katura, H. Atsumori, Y. Nishimura, A. Kinoshita, M. Kiguchi, H. Koizumi, M. Fukuda, and K. Kasai, “A NIRS-fMRI investigation of prefrontal cortex activity during a working memory task,” Neuroimage 83, 158–173 (2013).
[Crossref] [PubMed]

Babiloni, C.

K. J. Blinowska, F. Rakowski, M. Kaminski, F. D. V. Fallani, C. Del Percio, R. Lizio, and C. Babiloni, “Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms,” Clin. Neurophysiol. 128, 667–680 (2016).
[PubMed]

Baccalá, L. A.

L. A. Baccalá and K. Sameshima, “Overcoming the limitations of correlation analysis for many simultaneously processed neural structures,” Prog. Brain Res. 130, 33–47 (2001).
[Crossref] [PubMed]

L. A. Baccalá and K. Sameshima, “Partial directed coherence: a new concept in neural structure determination,” Biol. Cybern. 84(6), 463–474 (2001).
[Crossref] [PubMed]

K. Sameshima and L. A. Baccalá, “Using partial directed coherence to describe neuronal ensemble interactions,” J. Neurosci. Methods 94(1), 93–103 (1999).
[Crossref] [PubMed]

Bajaj, S.

S. Bajaj, D. Drake, A. J. Butler, and M. Dhamala, “Oscillatory motor network activity during rest and movement: an fNIRS study,” Front. Syst. Neurosci. 8, 13 (2014).
[Crossref] [PubMed]

Bandettini, P. A.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

S. M. Rao, P. A. Bandettini, J. R. Binder, J. A. Bobholz, T. A. Hammeke, E. A. Stein, and J. S. Hyde, “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex,” J. Cereb. Blood Flow Metab. 16(6), 1250–1254 (1996).
[Crossref] [PubMed]

Bang, O. Y.

C. H. Park, W. H. Chang, S. H. Ohn, S. T. Kim, O. Y. Bang, A. Pascual-Leone, and Y. H. Kim, “Longitudinal changes of resting-state functional connectivity during motor recovery after stroke,” Stroke 42(5), 1357–1362 (2011).
[Crossref] [PubMed]

Barnett, L.

A. K. Seth, A. B. Barrett, and L. Barnett, “Granger causality analysis in neuroscience and neuroimaging,” J. Neurosci. 35(8), 3293–3297 (2015).
[Crossref] [PubMed]

L. Barnett and A. K. Seth, “Behaviour of Granger causality under filtering: theoretical invariance and practical application,” J. Neurosci. Methods 201(2), 404–419 (2011).
[Crossref] [PubMed]

Barrett, A. B.

A. K. Seth, A. B. Barrett, and L. Barnett, “Granger causality analysis in neuroscience and neuroimaging,” J. Neurosci. 35(8), 3293–3297 (2015).
[Crossref] [PubMed]

Berkelbach van der Sprenkel, J. W.

M. P. van Meer, K. van der Marel, K. Wang, W. M. Otte, S. El Bouazati, T. A. Roeling, M. A. Viergever, J. W. Berkelbach van der Sprenkel, and R. M. Dijkhuizen, “Recovery of sensorimotor function after experimental stroke correlates with restoration of resting-state interhemispheric functional connectivity,” J. Neurosci. 30(11), 3964–3972 (2010).
[Crossref] [PubMed]

Berman, K. F.

J. C. Dreher and K. F. Berman, “Fractionating the neural substrate of cognitive control processes,” Proc. Natl. Acad. Sci. U.S.A. 99(22), 14595–14600 (2002).
[Crossref] [PubMed]

Binder, J. R.

S. M. Rao, P. A. Bandettini, J. R. Binder, J. A. Bobholz, T. A. Hammeke, E. A. Stein, and J. S. Hyde, “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex,” J. Cereb. Blood Flow Metab. 16(6), 1250–1254 (1996).
[Crossref] [PubMed]

Binkofski, F.

V. Nedelko, T. Hassa, F. Hamzei, C. Weiller, F. Binkofski, M. A. Schoenfeld, O. Tüscher, and C. Dettmers, “Age-independent activation in areas of the mirror neuron system during action observation and action imagery. A fMRI study,” Restor. Neurol. Neurosci. 28(6), 737–747 (2010).
[PubMed]

Bisono, I. N.

S. Halim and I. N. Bisono, “Automatic seasonal auto regressive moving average models and unit root test detection,” International Journal of Management Science and Engineering Management 3(4), 266–274 (2008).

Biswal, B.

B. Biswal, F. Z. Yetkin, V. M. Haughton, and J. S. Hyde, “Functional connectivity in the motor cortex of resting human brain using echo-planar MRI,” Magn. Reson. Med. 34(4), 537–541 (1995).
[Crossref] [PubMed]

Blinowska, K. J.

K. J. Blinowska, F. Rakowski, M. Kaminski, F. D. V. Fallani, C. Del Percio, R. Lizio, and C. Babiloni, “Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms,” Clin. Neurophysiol. 128, 667–680 (2016).
[PubMed]

A. Korzeniewska, M. Mańczak, M. Kamiński, K. J. Blinowska, and S. Kasicki, “Determination of information flow direction among brain structures by a modified directed transfer function (dDTF) method,” J. Neurosci. Methods 125(1-2), 195–207 (2003).
[Crossref] [PubMed]

Boas, D. A.

R. J. Cooper, J. Selb, L. Gagnon, D. Phillip, H. W. Schytz, H. K. Iversen, M. Ashina, and D. A. Boas, “A systematic comparison of motion artifact correction techniques for functional near-infrared spectroscopy,” Front. Neurosci. 6, 147 (2012).
[Crossref] [PubMed]

R. C. Mesquita, M. A. Franceschini, and D. A. Boas, “Resting state functional connectivity of the whole head with near-infrared spectroscopy,” Biomed. Opt. Express 1(1), 324–336 (2010).
[Crossref] [PubMed]

T. J. Huppert, S. G. Diamond, M. A. Franceschini, and D. A. Boas, “HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain,” Appl. Opt. 48(10), D280–D298 (2009).
[Crossref] [PubMed]

G. Themelis, H. D’Arceuil, S. G. Diamond, S. Thaker, T. J. Huppert, D. A. Boas, and M. A. Franceschini, “Near-infrared spectroscopy measurement of the pulsatile component of cerebral blood flow and volume from arterial oscillations,” J. Biomed. Opt. 12(1), 014033 (2007).
[Crossref] [PubMed]

Bobholz, J. A.

S. M. Rao, P. A. Bandettini, J. R. Binder, J. A. Bobholz, T. A. Hammeke, E. A. Stein, and J. S. Hyde, “Relationship between finger movement rate and functional magnetic resonance signal change in human primary motor cortex,” J. Cereb. Blood Flow Metab. 16(6), 1250–1254 (1996).
[Crossref] [PubMed]

Bourdillon, N.

G. Derosière, F. Alexandre, N. Bourdillon, K. Mandrick, T. E. Ward, and S. Perrey, “Similar scaling of contralateral and ipsilateral cortical responses during graded unimanual force generation,” Neuroimage 85(Pt 1), 471–477 (2014).
[Crossref] [PubMed]

Bressler, S. L.

M. Kamiński, M. Ding, W. A. Truccolo, and S. L. Bressler, “Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance,” Biol. Cybern. 85(2), 145–157 (2001).
[Crossref] [PubMed]

Butler, A. J.

S. Bajaj, D. Drake, A. J. Butler, and M. Dhamala, “Oscillatory motor network activity during rest and movement: an fNIRS study,” Front. Syst. Neurosci. 8, 13 (2014).
[Crossref] [PubMed]

Calhoun, V. D.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

Chang, C.

R. M. Hutchison, T. Womelsdorf, E. A. Allen, P. A. Bandettini, V. D. Calhoun, M. Corbetta, S. Della Penna, J. H. Duyn, G. H. Glover, J. Gonzalez-Castillo, D. A. Handwerker, S. Keilholz, V. Kiviniemi, D. A. Leopold, F. de Pasquale, O. Sporns, M. Walter, and C. Chang, “Dynamic functional connectivity: promise, issues, and interpretations,” Neuroimage 80, 360–378 (2013).
[Crossref] [PubMed]

C. Chang and G. H. Glover, “Time-frequency dynamics of resting-state brain connectivity measured with fMRI,” Neuroimage 50(1), 81–98 (2010).
[Crossref] [PubMed]

Chang, W. H.

C. H. Park, W. H. Chang, S. H. Ohn, S. T. Kim, O. Y. Bang, A. Pascual-Leone, and Y. H. Kim, “Longitudinal changes of resting-state functional connectivity during motor recovery after stroke,” Stroke 42(5), 1357–1362 (2011).
[Crossref] [PubMed]

Chavez, M.

F. De Vico Fallani, J. Richiardi, M. Chavez, and S. Achard, “Graph analysis of functional brain networks: practical issues in translational neuroscience,” Philos. Trans. R. Soc. Lond. B Biol. Sci. 369(1653), 20130521 (2014).
[Crossref] [PubMed]

Chiosa, V.

V. Chiosa, S. A. Groppa, D. Ciolac, N. Koirala, L. Mişina, Y. Winter, M. Moldovanu, M. Muthuraman, and S. Groppa, “Breakdown of Thalamo-Cortical Connectivity Precedes Spike Generation in Focal Epilepsies,” Brain Connect. 7(5), 309–320 (2017).
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ACM Trans. Math. Softw. (1)

T. Schneider and A. Neumaier, “Algorithm 808: ARfit—A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models,” ACM Trans. Math. Softw. 27(1), 58–65 (2001).
[Crossref]

Annu. Rev. Physiol. (1)

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[Crossref] [PubMed]

Appl. Opt. (1)

Atten. Percept. Psychophys. (1)

A. M. Wing and A. B. Kristofferson, “Response delays and the timing of discrete motor responses,” Atten. Percept. Psychophys. 14(1), 5–12 (1973).
[Crossref]

Behav. Brain Res. (1)

B. Zhu and A. Godavarty, “Functional connectivity in the brain in joint attention skills using near infrared spectroscopy and imaging,” Behav. Brain Res. 250, 28–31 (2013).
[Crossref] [PubMed]

Biol. Cybern. (2)

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Figures (6)

Fig. 1
Fig. 1 On the left, fNIRS probes location using BrainNet Viewer [56] with transmitters (in red), receivers (in blue) and channels (Ch, in yellow). On the right, mean MNI coordinates and Brodmann area (BA) correspondence for each channel, to check to what extent fNIRS signals reflect the effect of several cerebral areas (represented in %) due to the spatial resolution of the system.
Fig. 2
Fig. 2 Flow chart of the various steps used for the TPDC analysis. The black arrows and rectangles (left side) present the overall process using real fNIRS data. The dotted gray arrows and rectangles (right side) display the bootstrapping process undertaken.
Fig. 3
Fig. 3 Example of time-frequency plots generated by TPDC analysis during 60 seconds of the finger-tapping task for 2 connections (From M1L to PMCL and PMCR to PMCL. a) Time frequency plot for the whole frequency band (5 Hertz). X-axis represents time in seconds. Y-axis the frequency in Hertz. Color bars represent the normalised (0 to 1) coherence of the connectivity extracted from TPDC results (blue close to zero connection and yellow close to 1 representing strong connection). b) Time frequency plot for the frequency band of interest [0.009 to 0.08 Hz]. c) Mean of the frequency band of interest. Y-axis represents strength of connection. This example shows higher connectivity strength from M1L to PMCL compared to PMCR to PMCL.
Fig. 4
Fig. 4 Whole surviving connections after bootstrapping analysis (n = 14). In blue, bi-directional connections (inter-hemispheric) and in red uni-directional connections located only in the contralateral hemisphere.
Fig. 5
Fig. 5 Average of the TPDC results with Box-plot, individual values and brain representation. Box-plot reflects median, quartile and dots of individuals values for each subject. X-axis represents connections. Y-axis is the mean TPDC value. Stars indicate Anova statistical significance at p = 0.05. On the right, representation of statistical significance on brain surfaces (differences are represented between red and blue connections).
Fig. 6
Fig. 6 Standard deviation and entropy of the TPDC results with Box-plot, individual values and brain representation. Box-plots in a) and b) reflect median, quartile and dots of individuals values for each subject. X-axis represents connections. Stars indicate Anova statistical significance at p = 0.05. a) STD TPDC. b) Entropy TPDC. c) Representation for both STD and Entropy of statistical significance on brain surfaces (differences are represented between red and blue connections).

Equations (6)

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ΔO D λ ij(t) =Ln[ Φ λ ij(0) Φ λ ij(t) ]
x(t)= r=1 r=p a r (t)x(tr)+η(t)
x(k)=F[ x(k1),w ]+Bυ(k)
y(k)=Cx(k)+n(k)
| π ij (λ) |= | A ij (λ) | k | A kj (λ) | 2
0 | π ij (λ) | 2 | 1

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