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

Development of a beam propagation method to simulate the point spread function degradation in scattering media

Scattering is one of the main issues that limit the imaging depth in deep tissue optical imaging. To characterize the role of scattering, we have developed a forward model based on the beam propagation method and established the link between the macroscopic optical properties of the media and the statistical parameters of the phase masks applied to the wavefront. Using this model, we have analyzed the degradation of the point-spread function of the illumination beam in the transition regime from ballistic to diffusive light transport. Our method provides a wave-optic simulation toolkit to analyze the effects of scattering on image quality degradation in scanning microscopy. Our open-source implementation is available at https://github.com/BUNPC/Beam-Propagation-Method.Accepted manuscrip

Contribution of speckle noise in near-infrared spectroscopy measurements

Near-infrared spectroscopy (NIRS) is widely used in biomedical optics with applications ranging from basic science, such as in functional neuroimaging, to clinical, as in pulse oximetry. Despite the relatively low absorption of tissue in the near-infrared, there is still a significant amount of optical attenuation produced by the highly scattering nature of tissue. Because of this, designers of NIRS systems have to balance source optical power and source–detector separation to maximize the signal-to-noise ratio (SNR). However, theoretical estimations of SNR neglect the effects of speckle. Speckle manifests as fluctuations of the optical power received at the detector. These fluctuations are caused by interference of the multiple random paths taken by photons in tissue. We present a model for the NIRS SNR that includes the effects of speckle. We performed experimental validations with a NIRS system to show that it agrees with our model. Additionally, we performed computer simulations based on the model to estimate the contribution of speckle noise for different collection areas and source–detector separations. We show that at short source–detector separation, speckle contributes most of the noise when using long coherence length sources. Considering this additional noise is especially important for hybrid applications that use NIRS and speckle contrast simultaneously, such as in diffuse correlation spectroscopy.R01 EB025145 - NIBIB NIH HHS; R24 NS104096 - NINDS NIH HHSPublished versio

Insight into the fundamental trade-offs of diffusion MRI from polarization-sensitive optical coherence tomography in ex vivo human brain

In the first study comparing high angular resolution diffusion MRI (dMRI) in the human brain to axonal orientation measurements from polarization-sensitive optical coherence tomography (PSOCT), we compare the accuracy of orientation estimates from various dMRI sampling schemes and reconstruction methods. We find that, if the reconstruction approach is chosen carefully, single-shell dMRI data can yield the same accuracy as multi-shell data, and only moderately lower accuracy than a full Cartesian-grid sampling scheme. Our results suggest that current dMRI reconstruction approaches do not benefit substantially from ultra-high b-values or from very large numbers of diffusion-encoding directions. We also show that accuracy remains stable across dMRI voxel sizes of 1 ​mm or smaller but degrades at 2 ​mm, particularly in areas of complex white-matter architecture. We also show that, as the spatial resolution is reduced, axonal configurations in a dMRI voxel can no longer be modeled as a small set of distinct axon populations, violating an assumption that is sometimes made by dMRI reconstruction techniques. Our findings have implications for in vivo studies and illustrate the value of PSOCT as a source of ground-truth measurements of white-matter organization that does not suffer from the distortions typical of histological techniques.Published versio

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

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

Frequency domain near-infrared multiwavelength imager design using high-speed, direct analog-to-digital conversion

Frequency domain near-infrared spectroscopy (FD-NIRS) has proven to be a reliable method for quantification of tissue absolute optical properties. We present a full-sampling direct analog-to-digital conversion FD-NIR imager. While we developed this instrument with a focus on high-speed optical breast tomographic imaging, the proposed design is suitable for a wide-range of biophotonic applications where fast, accurate quantification of absolute optical properties is needed. Simultaneous dual wavelength operation at 685 and 830 nm is achieved by concurrent 67.5 and 75 MHz frequency modulation of each laser source, respectively, followed by digitization using a high-speed (180  MS/s) 16-bit A/D converter and hybrid FPGA-assisted demodulation. The instrument supports 25 source locations and features 20 concurrently operating detectors. The noise floor of the instrument was measured at <1.4  pW/√Hz, and a dynamic range of 115+ dB, corresponding to nearly six orders of magnitude, has been demonstrated. Titration experiments consisting of 200 different absorption and scattering values were conducted to demonstrate accurate optical property quantification over the entire range of physiologically expected values.United States. National Institutes of Health (R01-CA142575)United States. National Institutes of Health (R01-CA097305)United States. National Institutes of Health (R01-CA187595)United States. National Institutes of Health (R00-EB011889

Motion correction for phase-resolved dynamic optical coherence tomography imaging of rodent cerebral cortex

Cardiac and respiratory motions in animals are the primary source of image quality degradation in dynamic imaging studies, especially when using phase-resolved imaging modalities such as spectral-domain optical coherence tomography (SD-OCT), whose phase signal is very sensitive to movements of the sample. This study demonstrates a method with which to compensate for motion artifacts in dynamic SD-OCT imaging of the rodent cerebral cortex. We observed that respiratory and cardiac motions mainly caused, respectively, bulk image shifts (BISs) and global phase fluctuations (GPFs). A cross-correlation maximization-based shift correction algorithm was effective in suppressing BISs, while GPFs were significantly reduced by removing axial and lateral global phase variations. In addition, a non-origin-centered GPF correction algorithm was examined. Several combinations of these algorithms were tested to find an optimized approach that improved image stability from 0.5 to 0.8 in terms of the cross-correlation over 4 s of dynamic imaging, and reduced phase noise by two orders of magnitude in ~8% voxels.K99 NS067050 - NINDS NIH HHS; R01EB000790 - NIBIB NIH HHS; R01 EB001954 - NIBIB NIH HHS; R01 EB001954-09 - NIBIB NIH HHS; P01NS055104 - NINDS NIH HHS; R01 NS057476 - NINDS NIH HHS; K99NS067050 - NINDS NIH HHS; R01 EB000790 - NIBIB NIH HHS; R01-EB001954 - NIBIB NIH HHS; R01NS057476 - NINDS NIH HHS; P01 NS055104 - NINDS NIH HHS; P41 EB015896 - NIBIB NIH HHSPublished versio

Direct Estimation of Evoked Hemoglobin Changes by Multimodality Fusion Imaging

In the last two decades, both diffuse optical tomography (DOT) and blood oxygen level dependent (BOLD)-based functional magnetic resonance imaging (fMRI) methods have been developed as noninvasive tools for imaging evoked cerebral hemodynamic changes in studies of brain activity. Although these two technologies measure functional contrast from similar physiological sources, i.e., changes in hemoglobin levels, these two modalities are based on distinct physical and biophysical principles leading to both limitations and strengths to each method. In this work, we describe a unified linear model to combine the complimentary spatial, temporal, and spectroscopic resolutions of concurrently measured optical tomography and fMRI signals. Using numerical simulations, we demonstrate that concurrent optical and BOLD measurements can be used to create cross-calibrated estimates of absolute micromolar deoxyhemoglobin changes. We apply this new analysis tool to experimental data acquired simultaneously with both DOT and BOLD imaging during a motor task, demonstrate the ability to more robustly estimate hemoglobin changes in comparison to DOT alone, and show how this approach can provide cross-calibrated estimates of hemoglobin changes. Using this multimodal method, we estimate the calibration of the 3tesla BOLD signal to be −0.55%±0.40% signal change per micromolar change of deoxyhemoglobin