Exploring brain functional connectivity in rest and sleep states: a fNIRS study

Thien Nguyen; Olajide Babawale; Tae Kim; Hang Joon Jo; Hanli Liu; Jae Gwan Kim

doi:10.1038/s41598-018-33439-2

journal article Open Access Nov 01, 2018

Exploring brain functional connectivity in rest and sleep states: a fNIRS study

Thien Nguyen

Olajide Babawale Tae Kim

Hang Joon Jo

Hanli Liu Jae Gwan Kim

Scientific Reports Vol. 8 No. 1 · Springer Science and Business Media LLC

View at Publisher Save 10.1038/s41598-018-33439-2

Abstract

AbstractThis study investigates the brain functional connectivity in the rest and sleep states. We collected EEG, EOG, and fNIRS signals simultaneously during rest and sleep phases. The rest phase was defined as a quiet wake-eyes open (w_o) state, while the sleep phase was separated into three states; quiet wake-eyes closed (w_c), non-rapid eye movement sleep stage 1 (N1), and non-rapid eye movement sleep stage 2 (N2) using the EEG and EOG signals. The fNIRS signals were used to calculate the cerebral hemodynamic responses (oxy-, deoxy-, and total hemoglobin). We grouped 133 fNIRS channels into five brain regions (frontal, motor, temporal, somatosensory, and visual areas). These five regions were then used to form fifteen brain networks. A network connectivity was computed by calculating the Pearson correlation coefficients of the hemodynamic responses between fNIRS channels belonging to the network. The fifteen networks were compared across the states using the connection ratio and connection strength calculated from the normalized correlation coefficients. Across all fifteen networks and three hemoglobin types, the connection ratio was high in the w_c and N1 states and low in the w_o and N2 states. In addition, the connection strength was similar between the w_c and N1 states and lower in the w_o and N2 states. Based on our experimental results, we believe that fNIRS has a high potential to be a main tool to study the brain connectivity in the rest and sleep states.

Topics

No keywords indexed for this article. Browse by subject →

References

38

[1]

Functional connectivity in the motor cortex of resting human brain using echo‐planar mri

Bharat Biswal, F. Zerrin Yetkin, Victor M. Haughton et al.

Magnetic Resonance in Medicine 1995 10.1002/mrm.1910340409

[2]

Van Den Heuvel, M. P. & Hulshoff Pol, H. E. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20(8), 519–534, https://doi.org/10.1016/j.euroneuro.2010.03.008 (2010). 10.1016/j.euroneuro.2010.03.008

[3]

Greicius, M. D., Krasnow, B., Reiss, A. L. & Menon, V. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci 100(1), 253–258, https://doi.org/10.1073/pnas.0135058100 (2003). 10.1073/pnas.0135058100

[4]

Beckmann, C. F., DeLuca, M., Devlin, J. T. & Smith, S. M. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Society of London B: Biological Sciences 360(1457), 1001–1013, https://doi.org/10.1098/rstb.2005.1634 (2005). 10.1098/rstb.2005.1634

[5]

Damoiseaux, J. S. et al. Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci 103(37), 13848–13853, https://doi.org/10.1073/pnas.0601417103 (2006). 10.1073/pnas.0601417103

[6]

Greicius, M. D., Srivastava, G., Reiss, A. L. & Menon, V. Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci USA 101(13), 4637–4642, https://doi.org/10.1073/pnas.0308627101 (2004). 10.1073/pnas.0308627101

[7]

Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study

Serge A.R.B. Rombouts, Frederik Barkhof, Rutger Goekoop et al.

Human Brain Mapping 2005 10.1002/hbm.20160

[8]

Greicius, M. D. et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol psychiatry 62(5), 429–437, https://doi.org/10.1016/j.biopsych.2006.09.020 (2007). 10.1016/j.biopsych.2006.09.020

[9]

Rombouts, S. A. et al. Model-free group analysis shows altered BOLD FMRI networks in dementia. Hum brain mapp 30(1), 256–266, https://doi.org/10.1002/hbm.20505 (2009). 10.1002/hbm.20505

[10]

Whitfield-Gabrieli, S. et al. Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci 106(4), 1279–1284, https://doi.org/10.1073/pnas.0809141106 (2009). 10.1073/pnas.0809141106

[11]

fMRI resting state networks and their association with cognitive fluctuations in dementia with Lewy bodies

Luis R. Peraza, Marcus Kaiser, Michael Firbank et al.

NeuroImage: Clinical 2014 10.1016/j.nicl.2014.03.013

[12]

Lowe, M. J. et al. Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity. Hum brain mapp 29(7), 818–827, https://doi.org/10.1002/hbm.20576 (2008). 10.1002/hbm.20576

[13]

Ahn, S., Nguyen, T., Jang, H., Kim, J. G. & Jun, S. C. Exploring neuro-physiological correlates of drivers’ mental fatigue caused by sleep deprivation using simultaneous EEG, ECG, and fNIRS data. Front Hum Neurosci 10, https://doi.org/10.3389/fnhum.2016.00219 (2016). 10.3389/fnhum.2016.00219

[14]

Nguyen, T., Ahn, S., Jang, H., Jun, S. C. & Kim, J. G. Utilization of a combined EEG/NIRS system to predict driver drowsiness. Sci Rep 7, https://doi.org/10.1038/srep43933 (2017). 10.1038/srep43933

[15]

White, B. R. et al. Resting-state functional connectivity in the human brain revealed with diffuse optical tomography. Neuroimage 47(1), 148–156, https://doi.org/10.1016/j.neuroimage.2009.03.058 (2009). 10.1016/j.neuroimage.2009.03.058

[16]

Zhang, H. et al. Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements. Neuroimage 51(3), 1150–1161, https://doi.org/10.1016/j.neuroimage.2010.02.080 (2010). 10.1016/j.neuroimage.2010.02.080

[17]

Mesquita, R. C., Franceschini, M. A. & Boas, D. A. Resting state functional connectivity of the whole head with near-infrared spectroscopy. Biomed opt express 1(1), 324–336, https://doi.org/10.1364/BOE.1.000324 (2010). 10.1364/boe.1.000324

[18]

Lu, C. M. et al. Use of fNIRS to assess resting state functional connectivity. J of neurosci methods 186(2), 242–249, https://doi.org/10.1016/j.jneumeth.2009.11.010 (2010). 10.1016/j.jneumeth.2009.11.010

[19]

Molavi, B. et al. Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy. Front Hum Neurosci 7, 921, https://doi.org/10.3389/fnhum.2013.00921 (2014). 10.3389/fnhum.2013.00921

[20]

Wang, W. et al. Vigilance task-related change in brain functional connectivity as revealed by wavelet phase coherence analysis of near-infrared spectroscopy signals. Front Hum Neurosci 10, 400, https://doi.org/10.3389/fnhum.2016.00400 (2016). 10.3389/fnhum.2016.00400

[21]

Bu, L. et al. Effects of Sleep Deprivation on Phase Synchronization as Assessed by Wavelet Phase Coherence Analysis of Prefrontal Tissue Oxyhemoglobin Signals. PloS one 12(1), e0169279, https://doi.org/10.1371/journal.pone.0169279 (2017). 10.1371/journal.pone.0169279

[22]

Metzger, F. G. et al. Brain activation in frontotemporal and Alzheimer’s dementia: a functional near-infrared spectroscopy study. Alzheimer’s research & therapy 8(1), 56, https://doi.org/10.1186/s13195-016-0224-8 (2016). 10.1186/s13195-016-0224-8

[23]

Sämann, P. G. et al. Development of the brain’s default mode network from wakefulness to slow wave sleep. Cerebral cortex 21(9), 2082–2093, https://doi.org/10.1093/cercor/bhq295 (2011). 10.1093/cercor/bhq295

[24]

Horovitz, S. G. et al. Decoupling of the brain’s default mode network during deep sleep. Proceedings of the National Academy of Sciences 106(27), 11376–11381, https://doi.org/10.1073/pnas.0901435106 (2009). 10.1073/pnas.0901435106

[25]

Larson-Prior, L. J. et al. Cortical network functional connectivity in the descent to sleep. Proceedings of the National Academy of Sciences 106(11), 4489–4494, https://doi.org/10.1073/pnas.0900924106 (2009). 10.1073/pnas.0900924106

[26]

Ferri, R., Rundo, F., Bruni, O., Terzano, M. G. & Stam, C. J. Small-world network organization of functional connectivity of EEG slow-wave activity during sleep. Clin. Neurophysiol. 118, 449–456, https://doi.org/10.1016/j.clinph.2006.10.021 (2007). 10.1016/j.clinph.2006.10.021

[27]

Ferri, R., Rundo, F., Bruni, O., Terzano, M. G. & Stam, C. J. The functional connectivity of different EEG bands moves towards small-world network organization during sleep. Clin. Neurophysiol. 119, 2026–2036, https://doi.org/10.1016/j.clinph.2008.04.294 (2008). 10.1016/j.clinph.2008.04.294

[28]

Brodoehl, S., Klingner, C. M. & Witte, O. W. Eye closure enhances dark night perceptions. Sci Rep 5, 10515, https://doi.org/10.1038/srep10515 (2015). 10.1038/srep10515

[29]

Wolf, U. et al. Correlation of functional and resting state connectivity of cerebral oxy-, deoxy-, and total hemoglobin concentration changes measured by near-infrared spectrophotometry. J of biomed opt 16(8), 087013–087013, https://doi.org/10.1117/1.3615249 (2011). 10.1117/1.3615249

[30]

A Quantitative Comparison of Simultaneous BOLD fMRI and NIRS Recordings during Functional Brain Activation

Gary Strangman, Joseph P. Culver, John H. Thompson et al.

NeuroImage 2002 10.1006/nimg.2002.1227

[31]

Ye, J. C., Tak, S., Jang, K., Jung, J. & Jang, J. NIRS-SPM: statistical parametric mapping for near-infrared spectroscopy. Neuroimage 44(2), 428–447, https://doi.org/10.1016/j.neuroimage.2008.08.036 (2009). 10.1016/j.neuroimage.2008.08.036

[32]

HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain

Theodore J. Huppert, Solomon G. Diamond, Maria A. Franceschini et al.

Applied Optics 2009 10.1364/ao.48.00d280

[33]

Zaidi, A. D. et al. Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates. NeuroImage 120, 394–399, https://doi.org/10.1016/j.neuroimage.2015.07.019 (2015). 10.1016/j.neuroimage.2015.07.019

[34]

Virtanen, J., Noponen, T. & Meriläinen, P. Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals. J biomed opt 14(5), 054032–054032, https://doi.org/10.1117/1.3253323 (2009). 10.1117/1.3253323

[35]

Scholkmann, F. & Wolf, M. General equation for the differential pahtlength factor of the frontal human head depending on wavelength and age. J Biomed Opt 18(10), 105004, https://doi.org/10.1117/1.JBO.18.10.105004 (2013). 10.1117/1.jbo.18.10.105004

[36]

EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

Arnaud Delorme, Scott Makeig

Journal of Neuroscience Methods 2004 10.1016/j.jneumeth.2003.10.009

[37]

Iber C., Ancoli-Isreal S., Chesson A. & Quan S. F. The AASM manual for the scoring of sleep and associated events: Rules, terminology and technical specifications, 1st ed. Westchester, Illinois: American Academy of Sleep Medicine (2007).

[38]

BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics

Mingrui Xia, Jinhui Wang, Yong He

PLoS ONE 2013 10.1371/journal.pone.0068910

Cited By

77

Sleep indicators and staging: A functional near-infrared spectroscopy study in healthy young adults

Yong Cao, Xingwei An · 2025

NeuroImage

Metrics

77

Citations

38

References

Details

Published: Nov 01, 2018
Vol/Issue: 8(1)
License: View

Authors

Department of Hospital Administration, Graduate School of Public Health, Yonsei University, Seoul 03722, Korea; Institute of Health Services Research, Yonsei University, Seoul 03722, Korea

H

Hang Joon Jo

Departments of Neurologic Surgery,; Radiology, and; Neurology, and; Department of Physiology, College of Medicine, Hanyang University, Seoul, Republic of Korea

Funding

National Research Foundation of Korea Award: 2016M3C7A1905475 (JGK)

Cite This Article

Thien Nguyen, Olajide Babawale, Tae Kim, et al. (2018). Exploring brain functional connectivity in rest and sleep states: a fNIRS study. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-33439-2

Exploring brain functional connectivity in rest and sleep states: a fNIRS study

You May Also Like