Naturalistic stimuli, such as movies, more closely recapitulate “real life” sensory processing and behavioral demands relative to paradigms that rely on highly distilled and repetitive stimulus presentations. The rich complexity inherent in naturalistic stimuli demands an imaging system capable of measuring spatially distributed brain responses, and analysis tools optimized for unmixing responses to concurrently presented features. In this work, the combination of passive movie viewing with high-density diffuse optical tomography (HD-DOT) is developed as a platform for naturalistic brain mapping. We imaged healthy young adults during free viewing of a feature film using HD-DOT and observed reproducible, synchronized cortical responses across a majority of the field-of-view, most prominently in hierarchical cortical areas related to visual and auditory processing, both within and between individuals. In order to more precisely interpret broad patterns of cortical synchronization, we extracted visual and auditory features from the movie stimulus and mapped the cortical responses to the features. The results demonstrate the sensitivity of HD-DOT to evoked responses during naturalistic viewing, and that feature-based decomposition strategies enable functional mapping of naturalistic stimulus processing, including human-generated speech.

Motion‐induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high‐density diffuse optical tomography (HD‐DOT) with hundreds to thousands of source‐detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near‐infrared spectroscopy (fNIRS). This limitation restricts the application of HD‐DOT in many challenging imaging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluated a new motion detection method for multi‐channel optical imaging systems that leverages spatial patterns across measurement channels. Specifically, we introduced a global variance of temporal derivatives (GVTD) metric as a motion detection index. We showed that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion—with an area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we showed that applying GVTD‐based motion censoring on both hearing words task and resting state HD‐DOT data with natural head motion results in an improved spatial similarity to fMRI mapping. We then compared the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation‐based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring on HD‐DOT data outperforms other methods and results in spatial maps more similar to those of matched fMRI data.

Brain function can be assessed from resting-state functional connectivity (rs-fc) maps, most commonly created by analyzing the dynamics of cerebral hemoglobin concentration. Here, we develop the use of Laser Speckle Contrast Imaging (LSCI) for mapping rs-fc using cerebral blood flow (CBF) dynamics. Because LSCI is intrinsically noisy, we used spatial and temporal averaging to sufficiently raise the signal-to-noise ratio for observing robust functional networks. Although CBF-based rs-fc maps in healthy mice are qualitatively similar to simultaneously-acquired [HbO2]-based maps, some quantitative regional differences were observed. These combined flow/concentration maps might help clarify mechanisms involved in network disruption during disease.

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 smaller cross-sectional area than current high-density diffuse optical tomography (HD-DOT) systems. This superpixel sCMOS DOT (SP-DOT) system uses -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 ( ), high frame rate ( ), and a low effective detectivity threshold ( ), each comparable with previous HD-DOT systems. To assess system performance, we present retinotopic mapping of the visual cortex ( subjects). SP-DOT offers a practical solution to providing a wearable, large field-of-view, and high-resolution optical neuroimaging system.

Motion-induced artifacts can significantly corrupt optical neuroimaging, as in most neuroimaging modalities. For high-density diffuse optical tomography (HD-DOT) with hundreds to thousands of source-detector pair measurements, motion detection methods are underdeveloped relative to both functional magnetic resonance imaging (fMRI) and standard functional near-infrared spectroscopy (fNIRS). This limitation restricts the application of HD-DOT in many challenging situations and subject populations (e.g., bedside monitoring and children). Here, we evaluate a new motion detection method for multichannel optical imaging systems that leverages spatial patterns across channels. Specifically, we introduce a global variance of temporal derivatives (GVTD) metric as a motion detection index. We show that GVTD strongly correlates with external measures of motion and has high sensitivity and specificity to instructed motion - with area under the receiver operator characteristic curve of 0.88, calculated based on five different types of instructed motion. Additionally, we show that applying GVTD-based motion censoring on both task and resting state HD-DOT data with natural head motion results in an improved spatial similarity to fMRI mapping for the same respective protocols (task or rest). We then compare the GVTD similarity scores with several commonly used motion correction methods described in the fNIRS literature, including correlation-based signal improvement (CBSI), temporal derivative distribution repair (TDDR), wavelet filtering, and targeted principal component analysis (tPCA). We find that GVTD motion censoring outperforms other methods and results in spatial maps more similar to matched fMRI data.

No abstract

Functional magnetic resonance imaging (fMRI) has dramatically advanced non-invasive human brain mapping and decoding. Functional near-infrared spectroscopy (fNIRS) and high-density diffuse optical tomography (HD-DOT) non-invasively measure blood oxygen fluctuations related to brain activity, like fMRI, at the brain surface, using more-lightweight equipment that circumvents ergonomic and logistical limitations of fMRI. HD-DOT grids have smaller inter-optode spacing (~13 mm) than sparse fNIRS (~30 mm) and therefore provide higher image quality, with spatial resolution ~1/2 that of fMRI. Herein, simulations indicated reducing inter-optode spacing to 6.5 mm would further improve image quality and noise-resolution tradeoff, with diminishing returns below 6.5 mm. We then constructed an ultra-high-density DOT system (6.5-mm spacing) with 140 dB dynamic range that imaged stimulus-evoked activations with 30-50% higher spatial resolution and repeatable multi-focal activity with excellent agreement with participant-matched fMRI. Further, this system decoded visual stimulus position with 19-35% lower error than previous HD-DOT, throughout occipital cortex.

. Significance: The critical closing pressure (CrCP) of cerebral circulation, as measured by diffuse correlation spectroscopy (DCS), is a promising biomarker of intracranial hypertension. However, CrCP techniques using DCS have not been assessed in gold standard experiments. Aim: CrCP is typically calculated by examining the variation of cerebral blood flow (CBF) during the cardiac cycle (with normal sinus rhythm). We compare this typical CrCP measurement with a gold standard obtained during the drops in arterial blood pressure (ABP) caused by rapid ventricular pacing (RVP) in patients undergoing invasive electrophysiologic procedures. Approach: Adults receiving electrophysiology procedures with planned ablation were enrolled for DCS CBF monitoring. CrCP was calculated from CBF and ABP data by three methods: (1) linear extrapolation of data during RVP ( ; the gold standard); (2) linear extrapolation of data during regular heartbeats ( ); and (3) fundamental harmonic Fourier filtering of data during regular heartbeats ( ). Results: CBF monitoring was performed prior to and during 55 episodes of RVP in five adults. and demonstrated agreement ( , (95%CI, 0.72 to 1.38). Agreement between and was worse; was higher than (mean ± SD; ). Conclusions: Our results suggest that DCS-measured CrCP can be accurately acquired during normal sinus rhythm.