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

This 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.

[1]  David A. Boas,et al.  A Quantitative Comparison of Simultaneous BOLD fMRI and NIRS Recordings during Functional Brain Activation , 2002, NeuroImage.

[2]  Sangtae Ahn,et al.  Exploring Neuro-Physiological Correlates of Drivers' Mental Fatigue Caused by Sleep Deprivation Using Simultaneous EEG, ECG, and fNIRS Data , 2016, Front. Hum. Neurosci..

[3]  Guy A. Dumont,et al.  Analyzing the resting state functional connectivity in the human language system using near infrared spectroscopy , 2014, Front. Hum. Neurosci..

[4]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[5]  A. Chesson,et al.  The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Techinical Specifications , 2007 .

[6]  Lingguo Bu,et al.  Effects of Sleep Deprivation on Phase Synchronization as Assessed by Wavelet Phase Coherence Analysis of Prefrontal Tissue Oxyhemoglobin Signals , 2017, PloS one.

[7]  Stefan Brodoehl,et al.  Eye closure enhances dark night perceptions , 2015, Scientific Reports.

[8]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[9]  A. Braun,et al.  Decoupling of the brain's default mode network during deep sleep , 2009, Proceedings of the National Academy of Sciences.

[10]  Frederik Barkhof,et al.  Model‐free group analysis shows altered BOLD FMRI networks in dementia , 2009, Human brain mapping.

[11]  C. Stam,et al.  Small-world network organization of functional connectivity of EEG slow-wave activity during sleep , 2007, Clinical Neurophysiology.

[12]  M. Raichle,et al.  Cortical network functional connectivity in the descent to sleep , 2009, Proceedings of the National Academy of Sciences.

[13]  M. Lowe,et al.  Resting state sensorimotor functional connectivity in multiple sclerosis inversely correlates with transcallosal motor pathway transverse diffusivity , 2008, Human brain mapping.

[14]  M. Czisch,et al.  Development of the brain's default mode network from wakefulness to slow wave sleep. , 2011, Cerebral cortex.

[15]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[16]  D. Boas,et al.  Resting state functional connectivity of the whole head with near-infrared spectroscopy , 2010, Biomedical optics express.

[17]  Ann-Christine Ehlis,et al.  Brain activation in frontotemporal and Alzheimer’s dementia: a functional near-infrared spectroscopy study , 2016, Alzheimer's Research & Therapy.

[18]  M. P. van den Heuvel,et al.  Exploring the brain network: a review on resting-state fMRI functional connectivity. , 2010, European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology.

[19]  S. Rombouts,et al.  Altered resting state networks in mild cognitive impairment and mild Alzheimer's disease: An fMRI study , 2005, Human brain mapping.

[20]  M. Greicius,et al.  Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI , 2004, Proc. Natl. Acad. Sci. USA.

[21]  Chaozhe Zhu,et al.  Use of fNIRS to assess resting state functional connectivity , 2010, Journal of Neuroscience Methods.

[22]  G. Glover,et al.  Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus , 2007, Biological Psychiatry.

[23]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[24]  Matthias H. J. Munk,et al.  Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates , 2015, NeuroImage.

[25]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[26]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[27]  Jae Gwan Kim,et al.  Utilization of a combined EEG/NIRS system to predict driver drowsiness , 2017, Scientific Reports.

[28]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[29]  D. Boas,et al.  HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. , 2009, Applied optics.

[30]  Sungho Tak,et al.  NIRS-SPM: Statistical parametric mapping for near-infrared spectroscopy , 2009, NeuroImage.

[31]  P. Meriläinen,et al.  Comparison of principal and independent component analysis in removing extracerebral interference from near-infrared spectroscopy signals. , 2009, Journal of biomedical optics.

[32]  C. Stam,et al.  The functional connectivity of different EEG bands moves towards small-world network organization during sleep , 2008, Clinical Neurophysiology.

[33]  Lingguo Bu,et al.  Vigilance Task-Related Change in Brain Functional Connectivity as Revealed by Wavelet Phase Coherence Analysis of Near-Infrared Spectroscopy Signals , 2016, Front. Hum. Neurosci..

[34]  J. O'Brien,et al.  MRI resting state networks and their association with cognitive uctuations in dementia with Lewy bodies uis , 2014 .

[35]  Martin Wolf,et al.  General equation for the differential pathlength factor of the frontal human head depending on wavelength and age , 2013, Journal of biomedical optics.

[36]  정진욱,et al.  Statistical parametric mapping for near infrared spectroscopy using general linear model , 2007 .

[37]  Martin Wolf,et al.  Correlation of functional and resting state connectivity of cerebral oxy-, deoxy-, and total hemoglobin concentration changes measured by near-infrared spectrophotometry. , 2011, Journal of biomedical optics.

[38]  Yufeng Zang,et al.  Functional connectivity as revealed by independent component analysis of resting-state fNIRS measurements , 2010, NeuroImage.

[39]  J. Gabrieli,et al.  Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia , 2009, Proceedings of the National Academy of Sciences.

[40]  Abraham Z. Snyder,et al.  Resting-state functional connectivity in the human brain revealed with diffuse optical tomography , 2009, NeuroImage.