Improving sensitivity of task-iMRI by use of reciprocal information from peripheral near infrared spectroscopy (NIRS) measurements

Functional magnetic resonance imaging (fMRI) is a popular brain imaging modality with a wide range of use in clinical research. A major problem with fMRI signals is the presence of systemic physiological fluctuations in the low frequency range (<0.15 Hz), which significantly lowers detection power for task induced hemodynamic responses to neuronal activation. In the present study, we evaluate a parsimonious and computationally efficient method for removing systemic low frequency oscillations (sLFOs) from task fMRI signals by taking into account a recently well established physiological phenomenon: their dynamic passage throughout the cerebral vasculature. We utilize fingertip HBO signal concurrently recorded with near infrared spectroscopy (NIRS) as a dynamic systemic noise modeling regressor in a general linear model and will examine improvements in a) functional contrast to noise ratio (CNR), b) sensitivity and c) specificity of the resulting fMRI activation maps at the single subject and group level. Our preliminary results suggest that a preprocessing pipeline utilizing dynamic fingertip HBO signal denoising after standard fMRI preprocessing steps explains variance to a greater extent, and provides improved activity localization with less false positives.