Initial-Dip Existence and Estimation in Relation to DPF and Data Drift

Near infrared spectroscopy (NIRS) is a promising technology for functional brain imaging which measures hemodynamic signals from the cortex, similar to functional magnetic resonance imaging (fMRI), but does not require the participant to lie motionless in a confined space. NIRS can therefore be used for more naturalistic experiments, including face to face communication, or natural body movements, and is well suited for real-time applications that may require lengthy training. However, improving signal quality and reducing noise, especially noise induced by head motion, is challenging, particularly for real time applications. Here we study the properties of head motion induced noise, and find that motion noise causes the measured oxygenated and deoxygenated hemoglobin signals, which are typically strongly negatively correlated, to become more positively correlated. Next, we develop a method to reduce noise based on the principle that the concentration changes of oxygenated and deoxygenated hemoglobin should be negatively correlated. We show that despite its simplicity, this method is effective in reducing noise and improving signal quality, for both online and offline noise reduction. Copyright 2009 Elsevier Inc. All rights reserved.

Neural activity in the brain is accompanied by changes in cerebral blood flow (CBF) and blood oxygenation that are detectable with functional magnetic resonance imaging (fMRI) techniques. In this paper, recent mathematical models of this hemodynamic response are reviewed and integrated. Models are described for: (1) the blood oxygenation level dependent (BOLD) signal as a function of changes in cerebral oxygen extraction fraction (E) and cerebral blood volume (CBV); (2) the balloon model, proposed to describe the transient dynamics of CBV and deoxy-hemoglobin (Hb) and how they affect the BOLD signal; (3) neurovascular coupling, relating the responses in CBF and cerebral metabolic rate of oxygen (CMRO(2)) to the neural activity response; and (4) a simple model for the temporal nonlinearity of the neural response itself. These models are integrated into a mathematical framework describing the steps linking a stimulus to the measured BOLD and CBF responses. Experimental results examining transient features of the BOLD response (post-stimulus undershoot and initial dip), nonlinearities of the hemodynamic response, and the role of the physiologic baseline state in altering the BOLD signal are discussed in the context of the proposed models. Quantitative modeling of the hemodynamic response, when combined with experimental data measuring both the BOLD and CBF responses, makes possible a more specific and quantitative assessment of brain physiology than is possible with standard BOLD imaging alone. This approach has the potential to enhance numerous studies of brain function in development, health, and disease.