Current gold standard neuroimaging tools lack either necessary temporal resolution (PET) or optimal safety due to contraindications (fMRI) for measuring the neural mechanisms underlying the effects of deep brain stimulation of the subthalamic nucleus (STN DBS) in Parkinson disease (PD). In this study, we validate the feasibility of High-Density Diffuse Optical Tomography (HD-DOT) for mapping the cortical activity of the PD patients with their STN DBS ON and OFF during auditory and visual tasks and during resting state.
Though optical imaging of human brain function is gaining momentum, widespread adoption is restricted in part by a tradeoff among cap wearability, field of view, and resolution. To increase coverage while maintaining functional magnetic resonance imaging (fMRI)-comparable image quality, optical systems require more fibers. However, these modifications drastically reduce the wearability of the imaging cap. The primary obstacle to optimizing wearability is cap weight, which is largely determined by fiber diameter. Smaller fibers collect less light and lead to challenges in obtaining adequate signal-to-noise ratio. Here, we report on a design that leverages the exquisite sensitivity of scientific CMOS cameras to use fibers with ∼30 × smaller cross-sectional area than current high-density diffuse optical tomography (HD-DOT) systems. This superpixel sCMOS DOT (SP-DOT) system uses 200-μm-diameter fibers that facilitate a lightweight, wearable cap. We developed a superpixel algorithm with pixel binning and electronic noise subtraction to provide high dynamic range (>105), high frame rate (>6 Hz), and a low effective detectivity threshold (∼200 fW / Hz1/2-mm2), each comparable with previous HD-DOT systems. To assess system performance, we present retinotopic mapping of the visual cortex (n = 5 subjects). SP-DOT offers a practical solution to providing a wearable, large field-of-view, and high-resolution optical neuroimaging system.
As with other imaging modalities, motion induces artifacts that can significantly corrupt optical neuroimaging data. While multiple methods have been developed for motion detection in individual NIRS measurement channels, the large measurement numbers present in multichannel fNIRS or high-density diffuse optical tomography (HD-DOT) systems, create an opportunity for detection methods that integrate over the entire field of view. Here, we leverage the inherent covariance among multiple NIRS measurements after pre-processing, to quantify motion artifacts by calculating the global variance in the temporal derivative (GV-TD) across all measurements (e.g. from the temporal derivative of each time-course, the method calculates root mean square across all measurements for each time point). This calculation is fast, automated, and identifies motion by incorporating global aspects of data instead of individual channels. To test the performance, we designed an experimental paradigm that intermixed controlled epochs of motion artifact with relatively motion-free epochs during a block design hearing-words language paradigm using a previously described HD-DOT system. We categorized 348 blocks by sorting the blocks based on the maximum of their GV-TD time-courses. Our results show that with a modest thresholding of the data, wherein we keep data with 0.66 of the full data set average GV-TD, we obtain a ~50% increase in the signal-to-noise. With noisier data, we expect the performance gains to increase. Further, the impact on resting state functional connectivity may also be more significant. In summary, a censoring threshold based on the GV-TD metric provides a fast and direct way for identifying motion artifacts.