Depth selectivity in time-domain fNIRS by analyzing moments and time windows

Sudakou, Aleh; Wabnitz, Heidrun; Lin, Ying-Hsi; Contini, Davide; Spinelli, Lorenzo; Torricelli, Alessandro; Liebert, Adam

doi:10.1117/12.2578518

Cited by 3 publications

(3 citation statements)

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“…These techniques are based on the parametrization of the measured DTOF: analysis of time windows, statistical moments, or Mellin-Laplace transform parameters. 43 , 179 , 180 Combining time-domain with distance-resolved NIRS or multi-wavelength measurements improves the method’s depth-selectivity, 181 , 182 including approaches based on two sources and two detectors that are arranged as described in the previous section for dual-slope measurements. 183 An approach based on the null-distance technique was proposed in which depth discrimination is based on the direct measurement of time spent by photons in the tissue when the inter-optode distance is very short.…”

Section: Data Analysis and Algorithmsmentioning

confidence: 99%

Optical imaging and spectroscopy for the study of the human brain: status report

et al. 2022

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. This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.

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Section: Data Analysis and Algorithmsmentioning

confidence: 99%

Optical imaging and spectroscopy for the study of the human brain: status report

et al. 2022

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“…[ 43 – 46 ]. Recent performance-assessment studies, including quantitative tests, found that moments outperform other measurands in terms of assessing Δ µ a in deep layers [ 47 – 49 ]. Using changes in moments at multiple wavelengths, which have slightly different depth sensitivity profiles, it was possible to estimate changes in the concentration of an injected indocyanine green (ICG) bolus in up to 24 layers [ 50 ].…”

Section: Introductionmentioning

confidence: 99%

Two-layered blood-lipid phantom and method to determine absorption and oxygenation employing changes in moments of DTOFs

Sudakou¹,

Wabnitz

Liemert

et al. 2023

Biomed. Opt. Express

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Near-infrared spectroscopy (NIRS) is an established technique for measuring tissue oxygen saturation (StO2), which is of high clinical value. For tissues that have layered structures, it is challenging but clinically relevant to obtain StO2 of the different layers, e.g. brain and scalp. For this aim, we present a new method of data analysis for time-domain NIRS (TD-NIRS) and a new two-layered blood-lipid phantom. The new analysis method enables accurate determination of even large changes of the absorption coefficient (Δµa) in multiple layers. By adding Δµa to the baseline µa, this method provides absolute µa and hence StO2 in multiple layers. The method utilizes (i) changes in statistical moments of the distributions of times of flight of photons (DTOFs), (ii) an analytical solution of the diffusion equation for an N-layered medium, (iii) and the Levenberg–Marquardt algorithm (LMA) to determine Δµa in multiple layers from the changes in moments. The method is suitable for NIRS tissue oximetry (relying on µa) as well as functional NIRS (fNIRS) applications (relying on Δµa). Experiments were conducted on a new phantom, which enabled us to simulate dynamic StO2 changes in two layers for the first time. Two separate compartments, which mimic superficial and deep layers, hold blood-lipid mixtures that can be deoxygenated (using yeast) and oxygenated (by bubbling oxygen) independently. Simultaneous NIRS measurements can be performed on the two-layered medium (variable superficial layer thickness, L), the deep (homogeneous), and/or the superficial (homogeneous). In two experiments involving ink, we increased the nominal µa in one of two compartments from 0.05 to 0.25 cm−1, L set to 14.5 mm. In three experiments involving blood (L set to 12, 15, or 17 mm), we used a protocol consisting of six deoxygenation cycles. A state-of-the-art multi-wavelength TD-NIRS system measured simultaneously on the two-layered medium, as well as on the deep compartment for a reference. The new method accurately determined µa (and hence StO2) in both compartments. The method is a significant progress in overcoming the contamination from the superficial layer, which is beneficial for NIRS and fNIRS applications, and may improve the determination of StO2 in the brain from measurements on the head. The advanced phantom may assist in the ongoing effort towards more realistic standardized performance tests in NIRS tissue oximetry. Data and MATLAB codes used in this study were made publicly available.

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“…Higher statistical moments of the DTOF present increased sensitivity to deeper tissue layers compared to signal intensity changes as the kernel for the moment calculation grows as a function of the time of flight. 15 Nevertheless, TD moment analysis reported in the literature still relies on block averaging to recover the hemodynamic response function (HRF) which, unlike the GLM, does not allow for the incorporation of additional knowledge of the shape of the HRF or the confounds in the signal.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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. 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.

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Depth selectivity in time-domain fNIRS by analyzing moments and time windows

Cited by 3 publications

References 10 publications

Optical imaging and spectroscopy for the study of the human brain: status report

Optical imaging and spectroscopy for the study of the human brain: status report

Two-layered blood-lipid phantom and method to determine absorption and oxygenation employing changes in moments of DTOFs

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

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