Resting-state functional connectivity measured by diffuse correlation spectroscopy

Dual-task related frontal cerebral blood flow changes in older adults with mild cognitive impairment: A functional diffuse correlation spectroscopy study

Introduction

In a worldwide aging population with a high prevalence of motor and cognitive impairment, it is paramount to improve knowledge about underlying mechanisms of motor and cognitive function and their interplay in the aging processes.

Methods

We measured prefrontal cerebral blood flow (CBF) using functional diffuse correlation spectroscopy during motor and dual-task. We aimed to compare CBF changes among 49 older adults with and without mild cognitive impairment (MCI) during a dual-task paradigm (normal walk, 2- forward count walk, 3-backward count walk, obstacle negotiation, and heel tapping). Participants with MCI walked slower during the normal walk and obstacle negotiation compared to participants with normal cognition (NC), while gait speed during counting conditions was not different between the groups, therefore the dual-task cost was higher for participants with NC. We built a linear mixed effects model with CBF measures from the right and left prefrontal cortex.

Results

MCI (n = 34) showed a higher increase in CBF from the normal walk to the 2-forward count walk (estimate = 0.34, 95% CI [0.02, 0.66], p = 0.03) compared to participants with NC, related to a right- sided activation. Both groups showed a higher CBF during the 3-backward count walk compared to the normal walk, while only among MCI, CFB was higher during the 2-forward count walk.

Discussion

Our findings suggest a differential prefrontal hemodynamic pattern in older adults with MCI compared to their NC counterparts during the dual-task performance, possibly as a response to increasing attentional demand.

Complete head cerebral sensitivity mapping for diffuse correlation spectroscopy using subject-specific magnetic resonance imaging models

We characterize cerebral sensitivity across the entire adult human head for diffuse correlation spectroscopy, an optical technique increasingly used for bedside cerebral perfusion monitoring. Sixteen subject-specific magnetic resonance imaging-derived head models were used to identify high sensitivity regions by running Monte Carlo light propagation simulations at over eight hundred uniformly distributed locations on the head. Significant spatial variations in cerebral sensitivity, consistent across subjects, were found. We also identified correlates of such differences suitable for real-time assessment. These variations can be largely attributed to changes in extracerebral thickness and should be taken into account to optimize probe placement in experimental settings.

First-in-clinical application of a time-gated diffuse correlation spectroscopy system at 1064 nm using superconducting nanowire single photon detectors in a neuro intensive care unit

Recently proposed time-gated diffuse correlation spectroscopy (TG-DCS) has significant advantages compared to conventional continuous wave (CW)-DCS, but it is still in an early stage and clinical capability has yet to be established. The main challenge for TG-DCS is the lower signal-to-noise ratio (SNR) when gating for the deeper traveling late photons. Longer wavelengths, such as 1064 nm have a smaller effective attenuation coefficient and a higher power threshold in humans, which significantly increases the SNR. Here, we demonstrate the clinical utility of TG-DCS at 1064 nm in a case study on a patient with severe traumatic brain injury admitted to the neuro-intensive care unit (neuroICU). We showed a significant correlation between TG-DCS early (ρ = 0.67) and late (ρ = 0.76) gated against invasive thermal diffusion flowmetry. We also analyzed TG-DCS at high temporal resolution (50 Hz) to elucidate pulsatile flow data. Overall, this study demonstrates the first clinical translation capability of the TG-DCS system at 1064 nm using a superconducting nanowire single-photon detector.

Quantification of perfusion and metabolism in an autism mouse model assessed by diffuse correlation spectroscopy and near-infrared spectroscopy

There is a need for quantitative biomarkers for early diagnosis of autism. Cerebral blood flow and oxidative metabolism parameters may show superior contrasts for improved characterization. Diffuse correlation spectroscopy (DCS) has been shown to be reliable method to obtain cerebral blood flow contrast in animals and humans. Thus, in this study, we evaluated the combination of DCS and fNIRS in an established autism mouse model. Our results indicate that autistic group had significantly (P = .001) lower (~40%) blood flow (1.16 ± 0.26) × 10^-8  cm² /s), and significantly (P = .015) lower (~70%) oxidative metabolism (52.4 ± 16.6 μmol/100 g/min) compared to control group ([1.93 ± 0.74] × 10^-8  cm² /s, 177.2 ± 45.8 μmol/100 g/min, respectively). These results suggest that the combination of DCS and fNIRS can provide hemodynamic and metabolic contrasts for in vivo assessment of autism pathological conditions noninvasively.

Noninvasive Optical Monitoring of Cerebral Blood Flow and EEG Spectral Responses after Severe Traumatic Brain Injury: A Case Report

Survivors of severe brain injury may require care in a neurointensive care unit (neuro-ICU), where the brain is vulnerable to secondary brain injury. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches in the neuro-ICU. Our goal is to address this need through combined measurements of EEG and functional optical spectroscopy (EEG-Optical) instrumentation and analysis to provide a complementary fusion of data about brain activity and function. We utilized the diffuse correlation spectroscopy method for assessing cerebral blood flow at the neuro-ICU in a patient with traumatic brain injury. The present case demonstrates the feasibility of continuous recording of noninvasive cerebral blood flow transients that correlated well with the gold-standard invasive measurements and with the frequency content changes in the EEG data.

Diffuse optical assessment of cerebral-autoregulation in older adults stratified by cerebrovascular risk

Diagnosis of cerebrovascular disease (CVD) at early stages is essential for preventing sequential complications. CVD is often associated with abnormal cerebral microvasculature, which may impact cerebral-autoregulation (CA). A novel hybrid near-infrared diffuse optical instrument and a finger plethysmograph were used to simultaneously detect low-frequency oscillations (LFOs) of cerebral blood flow (CBF), oxy-hemoglobin concentration ([HbO₂ ]), deoxy-hemoglobin concentration ([Hb]) and mean arterial pressure (MAP) in older adults before, during and after 70° head-up-tilting (HUT). The participants with valid data were divided based on Framingham risk score (FRS, 1-30 points) into low-risk (FRS ≤15, n = 13) and high-risk (FRS >15, n = 11) groups for developing CVD. The LFO gains were determined by transfer function analyses with MAP as the input, and CBF, [HbO₂ ] and [Hb] as the outputs (CA ∝ 1/Gain). At resting-baseline, LFO gains in the high-risk group were relatively lower compared to the low-risk group. The lower baseline gains in the high-risk group may attribute to compensatory mechanisms to maintain stronger steady-state CAs. However, HUT resulted in smaller gain reductions in the high-risk group compared to the low-risk group, suggesting weaker dynamic CAs. LFO gains are potentially valuable biomarkers for early detection of CVD based on associations with CAs.

Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.