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Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.

ABSTRACT: Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared. Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic "brain" responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches. Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative. Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.

SUBMITTER: Santosa H

PROVIDER: S-EPMC7511246 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.

Santosa Hendrik H Zhai Xuetong X Fishburn Frank F Sparto Patrick J PJ Huppert Theodore J TJ

Neurophotonics 20200701 3

<b>Significance:</b> Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data. <b>Aim:</b> The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were q ...[more]

PMID: 32995361

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Project description:Significance: There is a longstanding recommendation within the field of fNIRS to use oxygenated ( HbO2 ) and deoxygenated (HHb) hemoglobin when analyzing and interpreting results. Despite this, many fNIRS studies do focus on HbO2 only. Previous work has shown that HbO2 on its own is susceptible to systemic interference and results may mostly reflect that rather than functional activation. Studies using both HbO2 and HHb to draw their conclusions do so with varying methods and can lead to discrepancies between studies. The combination of HbO2 and HHb has been recommended as a method to utilize both signals in analysis. Aim: We present the development of the hemodynamic phase correlation (HPC) signal to combine HbO2 and HHb as recommended to utilize both signals in the analysis. We use synthetic and experimental data to evaluate how the HPC and current signals used for fNIRS analysis compare. Approach: About 18 synthetic datasets were formed using resting-state fNIRS data acquired from 16 channels over the frontal lobe. To simulate fNIRS data for a block-design task, we superimposed a synthetic task-related hemodynamic response to the resting state data. This data was used to develop an HPC-general linear model (GLM) framework. Experiments were conducted to investigate the performance of each signal at different SNR and to investigate the effect of false positives on the data. Performance was based on each signal's mean T -value across channels. Experimental data recorded from 128 participants across 134 channels during a finger-tapping task were used to investigate the performance of multiple signals [ HbO2 , HHb, HbT, HbD, correlation-based signal improvement (CBSI), and HPC] on real data. Signal performance was evaluated on its ability to localize activation to a specific region of interest. Results: Results from varying the SNR show that the HPC signal has the highest performance for high SNRs. The CBSI performed the best for medium-low SNR. The next analysis evaluated how false positives affect the signals. The analyses evaluating the effect of false positives showed that the HPC and CBSI signals reflect the effect of false positives on HbO2 and HHb. The analysis of real experimental data revealed that the HPC and HHb signals provide localization to the primary motor cortex with the highest accuracy. Conclusions: We developed a new hemodynamic signal (HPC) with the potential to overcome the current limitations of using HbO2 and HHb separately. Our results suggest that the HPC signal provides comparable accuracy to HHb to localize functional activation while at the same time being more robust against false positives.

Project description:BACKGROUND:To maintain adequate oxygen delivery to tissue, resuscitation of critically ill patients is guided by assessing surrogate markers of perfusion. As there is no direct indicator of cerebral perfusion used in routine critical care, identifying an accurate strategy to monitor brain perfusion is paramount. Near-infrared spectroscopy (NIRS) is a non-invasive technique to quantify regional cerebral oxygenation (rSO2) that has been used for decades during cardiac surgery which has led to targeted algorithms to optimize rSO2 being developed. However, these targeted algorithms do not exist during critical care, as the physiological determinants of rSO2 during critical illness remain poorly understood. MATERIALS AND METHODS:This prospective observational study was an exploratory analysis of a nested cohort of patients within the CONFOCAL study ( NCT02344043 ) who received high-fidelity vital sign monitoring. Adult patients (??18?years) admitted <?24?h to a medical/surgical intensive care unit were eligible if they had shock and/or required mechanical ventilation. Patients underwent rSO2 monitoring with the FORESIGHT oximeter for 24?h, vital signs were concurrently recorded, and clinically ordered arterial blood gas samples and hemoglobin concentration were also documented. Simultaneous multiple linear regression was performed using all available predictors, followed by model selection using the corrected Akaike information criterion (AICc). RESULTS:Our simultaneous multivariate model included age, heart rate, arterial oxygen saturation, mean arterial pressure, pH, partial pressure of oxygen, partial pressure of carbon dioxide (PaCO2), and hemoglobin concentration. This model accounted for a significant proportion of variance in rSO2 (R2?=?0.58, p?<?0.01) and was significantly associated with PaCO2 (p?<?0.05) and hemoglobin concentration (p?<?0.01). Our selected regression model using AICc accounted for a significant proportion of variance in rSO2 (R2?=?0.54, p?<?0.01) and was significantly related to age (p?<?0.05), PaCO2 (p?< 0.01), hemoglobin (p?<?0.01), and heart rate (p?<?0.05). CONCLUSIONS:Known and established physiological determinants of oxygen delivery accounted for a significant proportion of the rSO2 signal, which provides evidence that NIRS is a viable modality to assess cerebral oxygenation in critically ill adults. Further elucidation of the determinants of rSO2 has the potential to develop a NIRS-guided resuscitation algorithm during critical illness. TRIAL REGISTRATION:This trial is registered on clinicaltrials.gov (Identifier: NCT02344043 ), retrospectively registered January 8, 2015.

Project description:Significance: Designing optode layouts is an essential step for functional near-infrared spectroscopy (fNIRS) experiments as the quality of the measured signal and the sensitivity to cortical regions-of-interest depend on how optodes are arranged on the scalp. This becomes particularly relevant for fNIRS-based brain-computer interfaces (BCIs), where developing robust systems with few optodes is crucial for clinical applications. Aim: Available resources often dictate the approach researchers use for optode-layout design. We investigated whether guiding optode layout design using different amounts of subject-specific magnetic resonance imaging (MRI) data affects the fNIRS signal quality and sensitivity to brain activation when healthy participants perform mental-imagery tasks typically used in fNIRS-BCI experiments. Approach: We compared four approaches that incrementally incorporated subject-specific MRI information while participants performed mental-calculation, mental-rotation, and inner-speech tasks. The literature-based approach (LIT) used a literature review to guide the optode layout design. The probabilistic approach (PROB) employed individual anatomical data and probabilistic maps of functional MRI (fMRI)-activation from an independent dataset. The individual fMRI (iFMRI) approach used individual anatomical and fMRI data, and the fourth approach used individual anatomical, functional, and vascular information of the same subject (fVASC). Results: The four approaches resulted in different optode layouts and the more informed approaches outperformed the minimally informed approach (LIT) in terms of signal quality and sensitivity. Further, PROB, iFMRI, and fVASC approaches resulted in a similar outcome. Conclusions: We conclude that additional individual MRI data lead to a better outcome, but that not all the modalities tested here are required to achieve a robust setup. Finally, we give preliminary advice to efficiently using resources for developing robust optode layouts for BCI and neurofeedback applications.

Dataset Information

Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.

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Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies.

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