ninjaCap: A fully customizable and 3D printable headgear for fNIRS and EEG brain imaging.

Significance: Accurate sensor placement is vital for non-invasive brain imaging, particularly for functional near infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT), which lack standardized layouts like EEG. Custom, manually prepared probe layouts on textile caps are often imprecise and labor-intensive. Aim: We introduce a method for creating personalized, 3D-printed headgear, enabling accurate translation of 3D brain coordinates to 2D printable panels for custom fNIRS and EEG sensor layouts, reducing costs and manual labor. Approach: Our approach uses atlas-based or subject-specific head models and a spring-relaxation algorithm for flattening 3D coordinates onto 2D panels, using 10-5 EEG coordinates for reference. This process ensures geometrical fidelity, crucial for accurate probe placement. Probe geometries and holder types are customizable and printed directly on the cap, making the approach agnostic to instrument manufacturers and probe types. Results: Our ninjaCap method offers 2.2±1.5 mm probe placement accuracy. Over the last five years, we have developed and validated this approach with over 50 cap models and 500 participants. A cloud-based ninjaCap generation pipeline along with detailed instructions is now available at openfnirs.org. Conclusions: The ninjaCap marks a significant advancement in creating individualized neuroimaging caps, reducing costs and labor while improving probe placement accuracy, thereby reducing variability in research.

How much do time-domain functional near-infrared spectroscopy (fNIRS) moments improve estimation of brain activity over traditional fNIRS?

Significance: Advances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF. Aims: We compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM. Approach: We augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with "short-separation regression" designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests. Results: We found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment. Conclusions: Properly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.

Multivariate Kalman filter regression of confounding physiological signals for real-time classification of fNIRS data.

Significance: Functional near-infrared spectroscopy (fNIRS) is a noninvasive technique for measuring hemodynamic changes in the human cortex related to neural function. Due to its potential for miniaturization and relatively low cost, fNIRS has been proposed for applications, such as brain-computer interfaces (BCIs). The relatively large magnitude of the signals produced by the extracerebral physiology compared with the ones produced by evoked neural activity makes real-time fNIRS signal interpretation challenging. Regression techniques incorporating physiologically relevant auxiliary signals such as short separation channels are typically used to separate the cerebral hemodynamic response from the confounding components in the signal. However, the coupling of the extra-cerebral signals is often noninstantaneous, and it is necessary to find the proper delay to optimize nuisance removal. Aim: We propose an implementation of the Kalman filter with time-embedded canonical correlation analysis for the real-time regression of fNIRS signals with multivariate nuisance regressors that take multiple delays into consideration. Approach: We tested our proposed method on a previously acquired finger tapping dataset with the purpose of classifying the neural responses as left or right. Results: We demonstrate computationally efficient real-time processing of 24-channel fNIRS data (400 samples per second per channel) with a two order of selective magnitude decrease in cardiac signal power and up to sixfold increase in the contrast-to-noise ratio compared with the nonregressed signals. Conclusion: The method provides a way to obtain better distinction of brain from non-brain signals in real time for BCI application with fNIRS.

Erratum: Improved accuracy of cerebral blood flow quantification in the presence of systemic physiology cross-talk using multi-layer Monte Carlo modeling.

[This corrects the article DOI: 10.1117/1.NPh.8.1.015001.].

The role of diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy in monitoring cerebral hemodynamics during hypothermic circulatory arrests.

OBJECTIVES: Real-time noninvasive monitoring of cerebral blood flow (CBF) during surgery is key to reducing mortality rates associated with adult cardiac surgeries requiring hypothermic circulatory arrest (HCA). We explored a method to monitor cerebral blood flow during different brain protection techniques using diffuse correlation spectroscopy (DCS), a noninvasive optical technique which, combined with frequency-domain near-infrared spectroscopy (FDNIRS), also provides a measure of oxygen metabolism. METHODS: We used DCS in combination with FDNIRS to simultaneously measure hemoglobin oxygen saturation (SO2), an index of cerebral blood flow (CBFi), and an index of cerebral metabolic rate of oxygen (CMRO2i) in 12 patients undergoing cardiac surgery with HCA. RESULTS: Our measurements revealed that a negligible amount of blood is delivered to the cerebral cortex during HCA with retrograde cerebral perfusion, indistinguishable from HCA-only cases (median CBFi drops of 93% and 95%, respectively) with consequent similar decreases in SO2 (mean decrease of 0.6 ± 0.1% and 0.9 ± 0.2% per minute, respectively); CBFi and SO2 are mostly maintained with antegrade cerebral perfusion; the relationship of CMRO2i to temperature is given by CMRO2i = 0.052e0.079T. CONCLUSIONS: FDNIRS-DCS is able to detect changes in CBFi, SO2, and CMRO2i with intervention and can become a valuable tool for optimizing cerebral protection during HCA.

Pilot Study on Dose-Dependent Effects of Transcranial Photobiomodulation on Brain Electrical Oscillations: A Potential Therapeutic Target in Alzheimer's Disease.

BACKGROUND: Transcranial photobiomodulation (tPBM) has recently emerged as a potential cognitive enhancement technique and clinical treatment for various neuropsychiatric and neurodegenerative disorders by delivering invisible near-infrared light to the scalp and increasing energy metabolism in the brain. OBJECTIVE: We assessed whether transcranial photobiomodulation with near-infrared light modulates cerebral electrical activity through electroencephalogram (EEG) and cerebral blood flow (CBF). METHODS: We conducted a single-blind, sham-controlled pilot study to test the effect of continuous (c-tPBM), pulse (p-tPBM), and sham (s-tPBM) transcranial photobiomodulation on EEG oscillations and CBF using diffuse correlation spectroscopy (DCS) in a sample of ten healthy subjects [6F/4 M; mean age 28.6±12.9 years]. c-tPBM near-infrared radiation (NIR) (830 nm; 54.8 mW/cm2; 65.8 J/cm2; 2.3 kJ) and p-tPBM (830 nm; 10 Hz; 54.8 mW/cm2; 33%; 21.7 J/cm2; 0.8 kJ) were delivered concurrently to the frontal areas by four LED clusters. EEG and DCS recordings were performed weekly before, during, and after each tPBM session. RESULTS: c-tPBM significantly boosted gamma (t = 3.02, df = 7, p < 0.02) and beta (t = 2.91, df = 7, p < 0.03) EEG spectral powers in eyes-open recordings and gamma power (t = 3.61, df = 6, p < 0.015) in eyes-closed recordings, with a widespread increase over frontal-central scalp regions. There was no significant effect of tPBM on CBF compared to sham. CONCLUSION: Our data suggest a dose-dependent effect of tPBM with NIR on cerebral gamma and beta neuronal activity. Altogether, our findings support the neuromodulatory effect of transcranial NIR.

Validation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients.

SIGNIFICANCE: Intracranial pressure (ICP), variability in perfusion, and resulting ischemia are leading causes of secondary brain injury in patients treated in the neurointensive care unit. Continuous, accurate monitoring of cerebral blood flow (CBF) and ICP guide intervention and ultimately reduce morbidity and mortality. Currently, only invasive tools are used to monitor patients at high risk for intracranial hypertension. AIM: Diffuse correlation spectroscopy (DCS), a noninvasive near-infrared optical technique, is emerging as a possible method for continuous monitoring of CBF and critical closing pressure (CrCP or zero-flow pressure), a parameter directly related to ICP. APPROACH: We optimized DCS hardware and algorithms for the quantification of CrCP. Toward its clinical translation, we validated the DCS estimates of cerebral blood flow index (CBFi) and CrCP in ischemic stroke patients with respect to simultaneously acquired transcranial Doppler ultrasound (TCD) cerebral blood flow velocity (CBFV) and CrCP. RESULTS: We found CrCP derived from DCS and TCD were highly linearly correlated (ipsilateral R2 = 0.77, p = 9 × 10 - 7; contralateral R2 = 0.83, p = 7 × 10 - 8). We found weaker correlations between CBFi and CBFV (ipsilateral R2 = 0.25, p = 0.03; contralateral R2 = 0.48, p = 1 × 10 - 3) probably due to the different vasculature measured. CONCLUSION: Our results suggest DCS is a valid alternative to TCD for continuous monitoring of CrCP.

Improved accuracy of cerebral blood flow quantification in the presence of systemic physiology cross-talk using multi-layer Monte Carlo modeling.

Significance: Contamination of diffuse correlation spectroscopy (DCS) measurements of cerebral blood flow (CBF) due to systemic physiology remains a significant challenge in the clinical translation of DCS for neuromonitoring. Tunable, multi-layer Monte Carlo-based (MC) light transport models have the potential to remove extracerebral flow cross-talk in cerebral blood flow index ( CBF i ) estimates. Aim: We explore the effectiveness of MC DCS models in recovering accurate CBF i changes in the presence of strong systemic physiology variations during a hypercapnia maneuver. Approach: Multi-layer slab and head-like realistic (curved) geometries were used to run MC simulations of photon propagation through the head. The simulation data were post-processed into models with variable extracerebral thicknesses and used to fit DCS multi-distance intensity autocorrelation measurements to estimate CBF i timecourses. The results of the MC CBF i values from a set of human subject hypercapnia sessions were compared with CBF i values estimated using a semi-infinite analytical model, as commonly used in the field. Results: Group averages indicate a gradual systemic increase in blood flow following a different temporal profile versus the expected rapid CBF response. Optimized MC models, guided by several intrinsic criteria and a pressure modulation maneuver, were able to more effectively separate CBF i changes from scalp blood flow influence than the analytical fitting, which assumed a homogeneous medium. Three-layer models performed better than two-layer ones; slab and curved models achieved largely similar results, though curved geometries were closer to physiological layer thicknesses. Conclusion: Three-layer, adjustable MC models can be useful in separating distinct changes in scalp and brain blood flow. Pressure modulation, along with reasonable estimates of physiological parameters, can help direct the choice of appropriate layer thicknesses in MC models.

Diffuse correlation spectroscopy and frequency-domain near-infrared spectroscopy for measuring microvascular blood flow in dynamically exercising human muscles.

In the last 20 yr, near-infrared diffuse correlation spectroscopy (DCS) has been developed for providing a noninvasive estimate of microvascular blood flow (BF) as a BF index (BFi) in the human skin, muscle, breast, brain, and other tissue types. In this study, we proposed a new motion correction algorithm for DCS-derived BFi able to remove motion artifacts during cycling exercise. We tested this algorithm on DCS data collected during cycling exercise and demonstrated that DCS can be used to quantify muscle BFi during dynamic high-intensity exercise. In addition, we measured tissue regional oxygen metabolic rate (MRO2i) by combining frequency-domain multidistance near-infrared spectroscopy (FDNIRS) oximetry with DCS flow measures. Recreationally active subjects (n = 12; 31 ± 8 yr, 183 ± 4 cm, 79 ± 10 kg) pedaled at 80-100 revolutions/min until volitional fatigue with a work rate increase of 30 W every 4 min. Exercise intensity was normalized in each subject to the cycling power peak (Wpeak). Both rectus femoris BFi and MRO2i increased from 15% up to 75% Wpeak and then plateaued to the end of the exercise. During the recovery at 30 W cycling power, BFi remained almost constant, whereas MRO2i started to decrease. The BFi/MRO2i plateau was associated with the rising of the lactate concentration, indicating the progressive involvement of the anaerobic metabolism. These findings further highlight the utility of DCS and FDNIRS oximetry as effective, reproducible, and noninvasive techniques to assess muscle BFi and MRO2i in real time during a dynamic exercise such as cycling.NEW & NOTEWORTHY To the best of our knowledge, this study is the first to demonstrate that diffuse correlation spectroscopy in combination with frequency-domain near-infrared spectroscopy can monitor human quadriceps microvascular blood flow and oxygen metabolism with high temporal resolution during a cycling exercise. The optically measured parameters confirm the expected relationship between blood flow, muscle oxidative metabolism, and lactate production during exercise.

Portable System for Time-Domain Diffuse Correlation Spectroscopy.

We introduce a portable system for clinical studies based on time-domain diffuse correlation spectroscopy (DCS). After evaluating different lasers and detectors, the final system is based on a pulsed laser with about 550 ps pulsewidth, a coherence length of 38 mm, and two types of single-photon avalanche diodes (SPAD). The higher efficiency of the red-enhanced SPAD maximizes detection of the collected light, increasing the signal-to-noise ratio, while the better timing response of the CMOS SPAD optimizes the selection of late photons and increases spatial resolution. We discuss component selection and performance, and we present a full characterization of the system, measurement stability, a phantom-based validation study, and preliminary in vivo results collected from the forearms and the foreheads of four healthy subjects. With this system, we are able to resolve blood flow changes 1 cm below the skin surface with improved depth sensitivity and spatial resolution with respect to continuous wave DCS.