Multiple optodes configuration for measuring the absolute hemodynamic response using spatially resolved spectroscopy method: An fNIRS study

The paper presents a method for measuring the absolute hemoglobin concentration using a multiple optodes configuration. The functional near-infrared spectroscopy (fNIRS) is used for obtaining the data of the right individual two-finger movement (i.e., thumb and index fingers) in the left motor cortex. The measured light intensities of four healthy male subjects are converted to the absolute hemoglobin concentration levels by using the spatially resolved spectroscopy approach. The t-value of each channel is computed for generating the activation brain map. It is noteworthy that our work is firstly about detection of the active positions in the same brain region using the fNIRS technique with multiple optodes configuration. The experimental results revealed that the proposed method could measure the absolute hemoglobin concentration. The active channels of individual two-finger movement were found in the different territories. However, it also had the active overlapped areas between the index and thumb finger movement.

[1]  H. Sandwith THE ASSOCIATION JOURNAL , 1854 .

[2]  B. Wilson,et al.  A diffusion theory model of spatially resolved, steady-state diffuse reflectance for the noninvasive determination of tissue optical properties in vivo. , 1992, Medical physics.

[3]  D. Boas,et al.  Determination of optical properties and blood oxygenation in tissue using continuous NIR light , 1995, Physics in medicine and biology.

[4]  A. Kleinschmidt,et al.  Somatotopy in the Human Motor Cortex Hand Area. A High‐Resolution Functional MRI Study , 1997, The European journal of neuroscience.

[5]  D T Delpy,et al.  In vivo measurements of the wavelength dependence of tissue-scattering coefficients between 760 and 900 nm measured with time-resolved spectroscopy. , 1997, Applied optics.

[6]  Jens Frahm,et al.  Functional somatotopy of finger representations in human primary motor cortex , 2003, Human brain mapping.

[7]  N. Kudo,et al.  Imaging of Regional Differences of Muscle Oxygenation during Exercise Using Spatially Resolved NIRS , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[8]  David T Delpy,et al.  Measurement of the absolute optical properties and cerebral blood volume of the adult human head with hybrid differential and spatially resolved spectroscopy , 2006, Physics in medicine and biology.

[9]  J. Kim,et al.  Variation of haemoglobin extinction coefficients can cause errors in the determination of haemoglobin concentration measured by near-infrared spectroscopy , 2007, Physics in medicine and biology.

[10]  Datian Ye,et al.  Non-invasive measurement and validation of tissue oxygen saturation covered with overlying tissues , 2008 .

[11]  Martin A. Lindquist,et al.  Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling , 2009, NeuroImage.

[12]  Babak Shadgan,et al.  Wireless near-infrared spectroscopy of skeletal muscle oxygenation and hemodynamics during exercise and ischemia , 2009 .

[13]  Keum-Shik Hong,et al.  Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy , 2010, Biomedical engineering online.

[14]  David A. Boas,et al.  Improved recovery of the hemodynamic response in diffuse optical imaging using short optode separations and state-space modeling , 2011, NeuroImage.

[15]  S. Ge,et al.  Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series , 2011, Neuroscience Letters.

[16]  Rolf B. Saager,et al.  Two-detector Corrected Near Infrared Spectroscopy (C-NIRS) detects hemodynamic activation responses more robustly than single-detector NIRS , 2011, NeuroImage.

[17]  Shuzhi Sam Ge,et al.  Detection of event-related hemodynamic response to neuroactivation by dynamic modeling of brain activity , 2012, NeuroImage.

[18]  S. Ge,et al.  fNIRS-based online deception decoding , 2012, Journal of neural engineering.

[19]  Keum-Shik Hong,et al.  Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface , 2014, Experimental Brain Research.

[20]  Keum-Shik Hong,et al.  Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface , 2013, Neuroscience Letters.

[21]  C. Elwell,et al.  A portable wireless near-infrared spatially resolved spectroscopy system for use on brain and muscle. , 2013, Medical engineering & physics.

[22]  Keum-Shik Hong,et al.  Linear parameter-varying model and adaptive filtering technique for detecting neuronal activities: an fNIRS study , 2013, Journal of neural engineering.

[23]  Keum-Shik Hong,et al.  Noise reduction in functional near-infrared spectroscopy signals by independent component analysis. , 2013, The Review of scientific instruments.

[24]  Keum-Shik Hong,et al.  Reduction of physiological effects in fNIRS waveforms for efficient brain-state decoding , 2014, Neuroscience Letters.

[25]  Keum-Shik Hong,et al.  Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface , 2014, Front. Hum. Neurosci..

[26]  Babak Shadgan,et al.  Diagnosis of testicular torsion using near infrared spectroscopy: A novel diagnostic approach. , 2014, Canadian Urological Association journal = Journal de l'Association des urologues du Canada.

[27]  K. Hong,et al.  Lateralization of music processing with noises in the auditory cortex: an fNIRS study , 2014, Front. Behav. Neurosci..

[28]  David A. Boas,et al.  Further improvement in reducing superficial contamination in NIRS using double short separation measurements , 2014, NeuroImage.

[29]  M. R. Bhutta,et al.  Note: three wavelengths near-infrared spectroscopy system for compensating the light absorbance by water. , 2014, The Review of scientific instruments.

[30]  Keum-Shik Hong,et al.  State-space models of impulse hemodynamic responses over motor, somatosensory, and visual cortices. , 2014, Biomedical optics express.

[31]  Keum-Shik Hong,et al.  fNIRS-based brain-computer interfaces: a review , 2015, Front. Hum. Neurosci..

[32]  Y. Kim,et al.  Classification of prefrontal and motor cortex signals for three-class fNIRS–BCI , 2015, Neuroscience Letters.