Improved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis

For the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.Published versio

The Possible Role of CO2 in Producing A Post-Stimulus CBF and BOLD Undershoot

Comprehending the underlying mechanisms of neurovascular coupling is important for understanding the pathogenesis of neurodegenerative diseases related to uncoupling. Moreover, it elucidates the casual relation between the neural signaling and the hemodynamic responses measured with various imaging modalities such as functional magnetic resonance imaging (fMRI). There are mainly two hypotheses concerning this mechanism: a metabolic hypothesis and a neurogenic hypothesis. We have modified recent models of neurovascular coupling adding the effects of both NO (nitric oxide) kinetics, which is a well-known neurogenic vasodilator, and CO2 kinetics as a metabolic vasodilator. We have also added the Hodgkin–Huxley equations relating the membrane potentials to sodium influx through the membrane. Our results show that the dominant factor in the hemodynamic response is NO, however CO2 is important in producing a brief post-stimulus undershoot in the blood flow response that in turn modifies the fMRI blood oxygenation level-dependent post-stimulus undershoot. Our results suggest that increased cerebral blood flow during stimulation causes CO2 washout which then results in a post-stimulus hypocapnia induced vasoconstrictive effect

TARGETED PRINCIPLE COMPONENT ANALYSIS: A NEW MOTION ARTIFACT CORRECTION APPROACH FOR NEAR-INFRARED SPECTROSCOPY

As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemodynamic responses. There are various methods for correction such as principle component analysis (PCA), wavelet-based filtering and spline interpolation. Here, we introduce a new approach to motion artifact correction, targeted principle component analysis (tPCA), which incorporates a PCA filter only on the segments of data identified as motion artifacts. It is expected that this will overcome the issues of filtering desired signals that plagues standard PCA filtering of entire data sets. We compared the new approach with the most effective motion artifact correction algorithms on a set of data acquired simultaneously with a collodion-fixed probe (low motion artifact content) and a standard Velcro probe (high motion artifact content). Our results show that tPCA gives statistically better results in recovering hemodynamic response function (HRF) as compared to wavelet-based filtering and spline interpolation for the Velcro probe. It results in a significant reduction in mean-squared error (MSE) and significant enhancement in Pearson’s correlation coefficient to the true HRF. The collodion-fixed fiber probe with no motion correction performed better than the Velcro probe corrected for motion artifacts in terms of MSE and Pearson’s correlation coefficient. Thus, if the experimental study permits, the use of a collodion-fixed fiber probe may be desirable. If the use of a collodion-fixed probe is not feasible, then we suggest the use of tPCA in the processing of motion artifact contaminated data

Using the General Linear Model to Improve Performance in fNIRS Single Trial Analysis and Classification: A Perspective

Within a decade, single trial analysis of functional Near Infrared Spectroscopy (fNIRS) signals has gained significant momentum, and fNIRS joined the set of modalities frequently used for active and passive Brain Computer Interfaces (BCI). A great variety of methods for feature extraction and classification have been explored using state-of-the-art Machine Learning methods. In contrast, signal preprocessing and cleaning pipelines for fNIRS often follow simple recipes and so far rarely incorporate the available state-of-the-art in adjacent fields. In neuroscience, where fMRI and fNIRS are established neuroimaging tools, evoked hemodynamic brain activity is typically estimated across multiple trials using a General Linear Model (GLM). With the help of the GLM, subject, channel, and task specific evoked hemodynamic responses are estimated, and the evoked brain activity is more robustly separated from systemic physiological interference using independent measures of nuisance regressors, such as short-separation fNIRS measurements. When correctly applied in single trial analysis, e.g., in BCI, this approach can significantly enhance contrast to noise ratio of the brain signal, improve feature separability and ultimately lead to better classification accuracy. In this manuscript, we provide a brief introduction into the GLM and show how to incorporate it into a typical BCI preprocessing pipeline and cross-validation. Using a resting state fNIRS data set augmented with synthetic hemodynamic responses that provide ground truth brain activity, we compare the quality of commonly used fNIRS features for BCI that are extracted from (1) conventionally preprocessed signals, and (2) signals preprocessed with the GLM and physiological nuisance regressors. We show that the GLM-based approach can provide better single trial estimates of brain activity as well as a new feature type, i.e., the weight of the individual and channel-specific hemodynamic response function (HRF) regressor. The improved estimates yield features with higher separability, that significantly enhance accuracy in a binary classification task when compared to conventional preprocessing—on average +7.4% across subjects and feature types. We propose to adapt this well-established approach from neuroscience to the domain of single-trial analysis and preprocessing wherever the classification of evoked brain activity is of concern, for instance in BCI

Specificity of hemodynamic brain responses to painful stimuli: a functional near-infrared spectroscopy study

Assessing pain in individuals not able to communicate (e.g. infants, under surgery, or following stroke) is difficult due to the lack of non-verbal objective measures of pain. Near-infrared spectroscopy (NIRS) being a portable, non-invasive and inexpensive method of monitoring cerebral hemodynamic activity has the potential to provide such a measure. Here we used functional NIRS to evaluate brain activation to an innocuous and a noxious electrical stimulus on healthy human subjects (n = 11). For both innocuous and noxious stimuli, we observed a signal change in the primary somatosensory cortex contralateral to the stimulus. The painful and non-painful stimuli can be differentiated based on their signal size and profile. We also observed that repetitive noxious stimuli resulted in adaptation of the signal. Furthermore, the signal was distinguishable from a skin sympathetic response to pain that tended to mask it. Our results support the notion that functional NIRS has a potential utility as an objective measure of pain.Published versio

Brain correlates of motor complexity during observed and executed actions

Recently, cortical areas with motor properties have attracted attention widely to their involvement in both action generation and perception. Inferior frontal gyrus (IFG), ventral premotor cortex (PMv) and inferior parietal lobule (IPL), presumably consisting of motor-related areas, are of particular interest, given that they respond to motor behaviors both when they are performed and observed. Converging neuroimaging evidence has shown the functional roles of IFG, PMv and IPL in action understanding. Most studies have focused on the effects of modulations in goals and kinematics of observed actions on the brain response, but little research has explored the effects of manipulations in motor complexity. To address this, we used fNIRS to examine the brain activity in the frontal, motor, parietal and occipital regions, aiming to better understand the brain correlates involved in encoding motor complexity. Twenty-one healthy adults executed and observed two hand actions that differed in motor complexity. We found that motor complexity sensitive brain regions were present in the pars opercularis IFG/PMv, primary motor cortex (M1), IPL/supramarginal gyrus and middle occipital gyrus (MOG) during action execution, and in pars opercularis IFG/PMv and M1 during action observation. Our findings suggest that the processing of motor complexity involves not only M1 but also pars opercularis IFG, PMv and IPL, each of which plays a critical role in action perception and execution.P01 HD064653 - NICHD NIH HHS; P41EB015896 - Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)Published versio

Morphine Attenuates fNIRS Signal Associated With Painful Stimuli in the Medial Frontopolar Cortex (medial BA 10)

Functional near infrared spectroscopy (fNIRS) is a non-invasive optical imaging method that provides continuous measure of cortical brain functions. One application has been its use in the evaluation of pain. Previous studies have delineated a deoxygenation process associated with pain in the medial anterior prefrontal region, more specifically, the medial Brodmann Area 10 (BA 10). Such response to painful stimuli has been consistently observed in awake, sedated and anesthetized patients. In this study, we administered oral morphine (15 mg) or placebo to 14 healthy male volunteers with no history of pain or opioid abuse in a crossover double blind design, and performed fNIRS scans prior to and after the administration to assess the effect of morphine on the medial BA 10 pain signal. Morphine is the gold standard for inhibiting nociceptive processing, most well described for brain effects on sensory and emotional regions including the insula, the somatosensory cortex (the primary somatosensory cortex, S1, and the secondary somatosensory cortex, S2), and the anterior cingulate cortex (ACC). Our results showed an attenuation effect of morphine on the fNIRS-measured pain signal in the medial BA 10, as well as in the contralateral S1 (although observed in a smaller number of subjects). Notably, the extent of signal attenuation corresponded with the temporal profile of the reported plasma concentration for the drug. No clear attenuation by morphine on the medial BA 10 response to innocuous stimuli was observed. These results provide further evidence for the role of medial BA 10 in the processing of pain, and also suggest that fNIRS may be used as an objective measure of drug-brain profiles independent of subjective reports

Towards neuroscience of the everyday world (NEW) using functional near infrared spectroscopy

Published in final edited form as: Curr Opin Biomed Eng. 2021 June ; 18: doi:10.1016/j.cobme.2021.100272.Functional near-infrared spectroscopy (fNIRS) assesses human brain activity by noninvasively measuring changes of cerebral hemoglobin concentrations caused by modulation of neuronal activity. Recent progress in signal processing and advances in system design, such as miniaturization, wearability, and system sensitivity, have strengthened fNIRS as a viable and cost-effective complement to functional magnetic resonance imaging, expanding the repertoire of experimental studies that can be performed by the neuroscience community. The availability of fNIRS and electroencephalography for routine, increasingly unconstrained, and mobile brain imaging is leading toward a new domain that we term “Neuroscience of the Everyday World” (NEW). In this light, we review recent advances in hardware, study design, and signal processing, and discuss challenges and future directions.U01EB029856 - National Institutes of HealthAccepted manuscrip

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

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