Near-infrared spectroscopy

Fiber-optic sensor for non-invasive monitoring of blood pressure and NIRS during MRI scanning

Due to its non-invasiveness and safety, functional magnetic resonance imaging (fMRI) has become a major tool in the investigation of brain activity. Task activation-related blood flow has greatly increased our knowledge of brain functionality during the past two decades since the pioneering work of Ogawa et al., 1990.

Recently, the focus of interest has been on detecting and modelling spontaneous fluctuations in brain activity, initially discovered by Biswal et al, 1995. Although the presence of these very low frequency fluctuations has been known for over a decade now, their origin is still largely unexplained. It has been proposed that these connectivity fluctuations arise from several sources, namely, electrophysiological, metabolic and vascular sources.

Figure 1 presents absorption spectra for oxyhemoglobin and deoxyhemoglobin, the two principal chromophores in human tissue, from the visible to the near-infrared region. Blood oxygen variations can be determined by measuring reflected light at two or more wavelengths chosen at different sides from the isobestic point of near 800 nm. Interesting wavelengths in this respect are 660 nm (red) and 800-900 nm (NIR). In addition, Vern et al used 590 nm to measure slow oscillations in the cortical blood volume of rabbits.

 Fig. 1. Absorption spectra for oxyhemoglobin and deoxyhemoglobin based on the table by Scott Prahl, Oregon Medical Laser Center.

The aim of the present work is to develop an fMRI-compatible NIRS (near infrared spectroscopy) system that can be integrated with simultaneous fMRI, electro-encephalography (EEG) and non-invasive blood pressure measurements (NIBP). An NIBP device has been designed and constructed in the Optoelectronic and Measurement Techniques Laboratory of the University of Oulu for the university's Department of Radiology.

   

Fig. 2. Optical fibres and the cantilever and block diagram of a transfer/receiver module of the NIBP-device.

Illustrated in Figure 3 is the proposed system, which correlates EEG, NIBP and NIRS signals to fMRI functional network data. The ultimate goal is to simultaneously measure the sources of brain activity fluctuations as well as to detect and quantify their relations. This is particularly important since the formation of BOLD signal fluctuations is not yet fully elucidated. Understanding the relationship between the known sources of fluctuations, i.e., electrophysiological, metabolic and vascular sources, is a prerequisite for utilizing the system in a clinical setting.