Near-infrared spectroscopy (NIRS) is a promising and rapidly developing technology with several unique features such as portability, low cost and multi-functionality. It is the only spectroscopic technique which is sensitive to tissue hemodynamics (slow signal) and neuronal activity (fast signal). One of the problems limiting its application is the contribution of superficial tissue layers in the recorded signal as well as other artifacts and systemic physiological signals termed """"""""global interference"""""""". Those undesirable effects are especially prominent in continuous-wave NIRS instruments, which measure the intensity of the optical signal. Our proposal is a response to RFA-RR-09-001 and aims to improve the sensitivity, selectivity and signal-to-noise ratio of the NIRS instruments through optimization of data acquisition using the advanced signal processing technique based on Independent Component Analysis (ICA). It includes 1) signal decomposition into the statistically independent components, 2) identification of artifactual components and 3) their removal through signal restoration. Our preliminary data show that the signal-to-noise ratio of the functionally relevant changes in the NIRS signal can be significantly improved through the ICA-based signal processing. As a result, functionally relevant transient changes in the optical signal can be reliably recorded with a temporal resolution in the millisecond range.
The aims of the proposed project are: 1) to develop improved instrumentation and data acquisition methods through ICA-based algorithms to reliably detect optical spectroscopic signals from the deeper layers of tissue in the area of interest;2) to demonstrate the effectiveness of the ICA method in neurophysiological experiments with healthy subjects measuring the hemodynamic and fast optical signals during cognitive tasks of rapid object recognition in visual and auditory modality;and 3) to implement the ICA method as a software toolbox to be used as an integral part of the NIRS instruments allowing the investigator to optimize the data acquisition setup during the experiment. We will develop, test, validate and characterize the proposed computational algorithm in terms of its effectiveness in increasing the functional capabilities of non-invasive NIRS technology. The study will establish the potential utility of the method for improved noninvasive assessment and monitoring of biological tissue in health and disease.
(provided by the applicant): Noninvasive optical imaging techniques, such as near-infrared spectroscopy (NIRS), offer a number of potential advantages over existing techniques such as MRI or PET for monitoring biological tissue. NIRS technology is insensitive to the presence of metal objects in the subject's body (a prohibiting factor for MRI studies), less sensitive to subject movement and relatively inexpensive. One of the limiting factors in the application of NIRS is the effect of superficial layers of tissue which obscure the contribution of the deeper layers and may create undesirable noisy components (artifacts). In the proposed studies, we will develop a computational method to improve the sensitivity and selectivity of the NIRS instruments based on a novel signal processing technique (Independent Component Analysis or ICA). The demonstrated successful application of de-noising and optimization algorithms would encourage further application of low-cost NIRS technology in a wide variety of possible applications in basic research and clinical practice. Our project will set a stage for further development of computational tools improving the NIRS technology and increasing its usefulness for monitoring biological tissue during various functional conditions. With increased sensitivity and selectivity, the NIRS technology will find numerous applications in the basic research as well as clinical studies of many types of pathology in a bedside setting.
|Medvedev, Andrei V (2014) Does the resting state connectivity have hemispheric asymmetry? A near-infrared spectroscopy study. Neuroimage 85 Pt 1:400-7|
|Mellem, Monika S; Friedman, Rhonda B; Medvedev, Andrei V (2013) Gamma- and theta-band synchronization during semantic priming reflect local and long-range lexical-semantic networks. Brain Lang 127:440-51|
|Mellem, Monika S; Bastiaansen, Marcel C M; Pilgrim, Lea K et al. (2012) Word class and context affect alpha-band oscillatory dynamics in an older population. Front Psychol 3:97|