Optimized multimodal functional magnetic resonance imaging/near-infrared spectroscopy probe for ultrahigh-resolution mapping.

Functional near-infrared spectroscopy (fNIRS) is an increasingly important noninvasive method in neuroscience due to its high temporal resolution and ability to independently measure oxy- and deoxy-hemoglobin. However, the relatively low spatial resolution of fNIRS makes it difficult to relate this signal to underlying anatomy. Simultaneous functional magnetic resonance imaging (fMRI) can complement fNIRS with superior spatial resolution and the ability to image the entire brain, providing additional information to improve fNIRS localization. However, current simultaneous fMRI/fNIRS acquisition methods are not optimal, due to the poor physical compatibility of existing MR coils and fNIRS optodes. Here, we present a technique to manufacture a true multimodal fMRI/fNIRS probe in which both modalities can be used with maximal sensitivity. To achieve this, we designed custom MR coils with integral fNIRS optodes using three-dimensional printing. This multimodal probe can be used to optimize spatial ([Formula: see text]) and temporal resolution (2.5 Hz) of fMRI, and it provides maximal MRI sensitivity, while allowing for high flexibility in the location and density of fNIRS optodes within the area of interest. Phantom and human data are shown to confirm the improvement in sensitivity in both modalities. This probe shows promise for addressing fundamental questions of the relation of fNIRS to physiology.

Low-frequency oscillations measured in the periphery with near-infrared spectroscopy are strongly correlated with blood oxygen level-dependent functional magnetic resonance imaging signals.

Low-frequency oscillations (LFOs) in the range of 0.01-0.15 Hz are commonly observed in functional imaging studies, such as blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) and functional near-infrared spectroscopy (fNIRS). Some of these LFOs are nonneuronal and are closely related to autonomic physiological processes. In the current study, we conducted a concurrent resting-state fMRI and NIRS experiment with healthy volunteers. LFO data was collected simultaneously at peripheral sites (middle fingertip and big toes) by NIRS, and centrally in the brain by BOLD fMRI. The cross-correlations of the LFOs collected from the finger, toes, and brain were calculated. Our data show that the LFOs measured in the periphery (NIRS signals) and in the brain (BOLD fMRI) were strongly correlated with varying time delays. This demonstrates that some portion of the LFOs actually reflect systemic physiological circulatory effects. Furthermore, we demonstrated that NIRS is effective for measuring the peripheral LFOs, and that these LFOs and the temporal shifts between them are consistent in healthy participants and may serve as useful biomarkers for detecting and monitoring circulatory dysfunction.

Isolating the sources of widespread physiological fluctuations in functional near-infrared spectroscopy signals.

Physiological fluctuations at low frequency (<0.1 Hz) are prominent in functional near-infrared spectroscopy (fNIRS) measurements in both resting state and functional task studies. In this study, we used the high spatial resolution and full brain coverage of functional magnetic resonance imaging (fMRI) to understand the origins and commonalities of these fluctuations. Specifically, we applied a newly developed method, regressor interpolation at progressive time delays, to analyze concurrently recorded fNIRS and fMRI data acquired both in a resting state study and in a finger tapping study. The method calculates the voxelwise correlations between blood oxygen level dependent (BOLD) fMRI and fNIRS signals with different time shifts and localizes the areas in the brain that highly correlate with the fNIRS signal recorded at the surface of the head. The results show the wide spatial distribution of this physiological fluctuation in BOLD data, both in task and resting states. The brain areas that are highly correlated with global physiological fluctuations observed by fNIRS have a pattern that resembles the venous system of the brain, indicating the blood fluctuation from veins on the brain surface might strongly contribute to the overall fNIRS signal.