Kalman estimator- and general linear model-based on-line brain activation mapping by near-infrared spectroscopy

BackgroundNear-infrared spectroscopy (NIRS) is a non-invasive neuroimaging technique that recently has been developed to measure the changes of cerebral blood oxygenation associated with brain activities. To date, for functional brain mapping applications, there is no standard on-line method for analysing NIRS data.MethodsIn this paper, a novel on-line NIRS data analysis framework taking advantages of both the general linear model (GLM) and the Kalman estimator is devised. The Kalman estimator is used to update the GLM coefficients recursively, and one critical coefficient regarding brain activities is then passed to a t-statistical test. The t-statistical test result is used to update a topographic brain activation map. Meanwhile, a set of high-pass filters is plugged into the GLM to prevent very low-frequency noises, and an autoregressive (AR) model is used to prevent the temporal correlation caused by physiological noises in NIRS time series. A set of data recorded in finger tapping experiments is studied using the proposed framework.ResultsThe obtained results suggest that the method can effectively track the task related brain activation areas, and prevent the noise distortion in the estimation while the experiment is running. Thereby, the potential of the proposed method for real-time NIRS-based brain imaging was demonstrated.ConclusionsThis paper presents a novel on-line approach for analysing NIRS data for functional brain mapping applications. This approach demonstrates the potential of a real-time-updating topographic brain activation map.

[1]  David A. Boas,et al.  A temporal comparison of BOLD, ASL, and NIRS hemodynamic responses to motor stimuli in adult humans , 2006, NeuroImage.

[2]  Huijuan Zhao,et al.  Maps of optical differential pathlength factor of human adult forehead, somatosensory motor and occipital regions at multi-wavelengths in NIR. , 2002, Physics in medicine and biology.

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

[4]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[5]  Seonghun Park,et al.  Modeling of a hydraulic engine mount for active pneumatic engine vibration control using the extended Kalman filter , 2009 .

[6]  V. Altuzar,et al.  Atmospheric pollution profiles in Mexico City in two different seasons , 2003 .

[7]  M. Tamura,et al.  Interpretation of near-infrared spectroscopy signals: a study with a newly developed perfused rat brain model. , 2001, Journal of applied physiology.

[8]  C. Büchel,et al.  Dynamic changes in effective connectivity characterized by variable parameter regression and kalman filtering , 1998, Human brain mapping.

[9]  Stéphane Perrey,et al.  Non-invasive NIR spectroscopy of human brain function during exercise. , 2008, Methods.

[10]  Shozo Kojima,et al.  Motion illusion activates the visual motion area of the brain: A near-infrared spectroscopy (NIRS) study , 2006, Brain Research.

[11]  Hyun-Chul Sohn,et al.  A Road-Adaptive LQG Control for Semi-Active Suspension Systems , 2001 .

[12]  Fumitoshi Matsuno,et al.  Energy-based control of axially translating beams: varying tension, varying speed, and disturbance adaptation , 2005, IEEE Transactions on Control Systems Technology.

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

[14]  Hellmuth Obrig,et al.  Towards a standard analysis for functional near-infrared imaging , 2004, NeuroImage.

[15]  David A. Boas,et al.  Physiological System Identification with the Kalman Filter in Diffuse Optical Tomography , 2005, MICCAI.

[16]  Keum-Shik Hong,et al.  Federated Information Mode-Matched Filters in ACC Environment , 2005 .

[17]  Keum Shik Hong.  An open-loop control for underactuated manipulators using oscillatory inputs: steering capability of an unactuated joint , 2002, IEEE Trans. Control. Syst. Technol..

[18]  C. Markham,et al.  Hemodynamics for Brain-Computer Interfaces , 2008, IEEE Signal Processing Magazine.

[19]  Epifanio Bagarinao,et al.  Estimation of general linear model coefficients for real-time application , 2003, NeuroImage.

[20]  Xiaofeng Zhang,et al.  A spatial and temporal comparison of hemodynamic signals measured using optical and functional magnetic resonance imaging during activation in the human primary visual cortex , 2007, NeuroImage.

[21]  Fumitaka Homae,et al.  Functional activation in diverse regions of the developing brain of human infants , 2008, NeuroImage.

[22]  D. Delpy,et al.  Methods of quantitating cerebral near infrared spectroscopy data. , 1988, Advances in experimental medicine and biology.

[23]  Archana K. Singh,et al.  Spatial registration of multichannel multi-subject fNIRS data to MNI space without MRI , 2005, NeuroImage.

[24]  A. Villringer,et al.  Beyond the Visible—Imaging the Human Brain with Light , 2003, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[25]  C. Mcgilchrist,et al.  Recursive Estimation of the General Linear Model with Dependent Errors , 1979 .

[26]  Anders M. Dale,et al.  Diffuse optical imaging of brain activation: approaches to optimizing image sensitivity, resolution, and accuracy , 2004, NeuroImage.

[27]  Keum-Shik Hong,et al.  Exponential Stabilization of an Axially Moving Tensioned Strip by Passive Damping and Boundary Control , 2002 .

[28]  Keum-Shik Hong,et al.  Vibrational stabilization of nonlinear parabolic systems with Neumann boundary conditions , 1991 .

[29]  G. Glover.  Deconvolution of Impulse Response in Event-Related BOLD fMRI1 , 1999, NeuroImage.

[30]  Ann-Christine Ehlis,et al.  Model-based analysis of rapid event-related functional near-infrared spectroscopy (NIRS) data: A parametric validation study , 2007, NeuroImage.

[31]  Guillaume Flandin,et al.  Functional optical signal analysis: a software tool for near-infrared spectroscopy data processing incorporating statistical parametric mapping. , 2007, Journal of biomedical optics.

[32]  A. Hielscher,et al.  Instrumentation for fast functional optical tomography , 2002 .

[33]  A. Maki,et al.  Optical topography: practical problems and new applications. , 2003, Applied optics.

[34]  F. Irani,et al.  Functional Near Infrared Spectroscopy (fNIRS): An Emerging Neuroimaging Technology with Important Applications for the Study of Brain Disorders , 2007, The Clinical neuropsychologist.

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

[36]  Keum Shik Hong,et al.  Vibrational control of nonlinear time lag systems: Vibrational stabilization and transient behavior , 1991, Autom..

[37]  C H Schmitz,et al.  Optical tomographic imaging of dynamic features of dense-scattering media. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[38]  Karl J. Friston,et al.  Statistical parametric mapping , 2013 .

[39]  A. Villringer,et al.  Spontaneous Low Frequency Oscillations of Cerebral Hemodynamics and Metabolism in Human Adults , 2000, NeuroImage.

[40]  N. Kuboyama,et al.  Human motor cortex oxygenation during exhaustive pinching task , 2007, Brain Research.

[41]  Meltem Izzetoglu,et al.  Motion artifact cancellation in NIR spectroscopy using discrete Kalman filtering , 2010, Biomedical engineering online.

[42]  Keum-Shik Hong,et al.  An IMM algorithm with federated information mode‐matched filters for AGV , 2007 .

[43]  Ville Kolehmainen,et al.  Time-series estimation of biological factors in optical diffusion tomography. , 2003, Physics in medicine and biology.

[44]  David A. Boas,et al.  Dynamic physiological modeling for functional diffuse optical tomography , 2006, NeuroImage.

[45]  Karl J. Friston,et al.  Analysis of fMRI Time-Series Revisited—Again , 1995, NeuroImage.

[46]  G Gratton,et al.  The event-related optical signal: a new tool for studying brain function. , 2001, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[47]  Theodore J. Huppert,et al.  Real-time imaging of human brain function by near-infrared spectroscopy using an adaptive general linear model , 2009, NeuroImage.

[48]  Martin Wolf,et al.  Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications. , 2007, Journal of biomedical optics.