Correction to: Functional near-infrared spectroscopy (fNIRS) brain imaging of multi-sensory integration during computerized dynamic posturography in middle-aged and older adults.

In the original publication, Figs. 2 and 3 are not placed in the correct order. Figure 2 should be replaced by Fig. 3 and Fig. 3 should be replaced by Fig. 2.

Functional near-infrared spectroscopy (fNIRS) brain imaging of multi-sensory integration during computerized dynamic posturography in middle-aged and older adults.

Studies suggest that aging affects the sensory re-weighting process, but the neuroimaging evidence is minimal. Functional Near-Infrared Spectroscopy (fNIRS) is a novel neuroimaging tool that can detect brain activities during dynamic movement condition. In this study, fNIRS was used to investigate the hemodynamic changes in the frontal-lateral, temporal-parietal, and occipital regions of interest (ROIs) during four sensory integration conditions that manipulated visual and somatosensory feedback in 15 middle-aged and 15 older adults. The results showed that the temporal-parietal ROI was activated more when somatosensory and visual information were absent in both groups, which indicated the sole use of vestibular input for maintaining balance. While both older adults and middle-aged adults had greater activity in most brain ROIs during changes in the sensory conditions, the older adults had greater increases in the occipital ROI and frontal-lateral ROIs. These findings suggest a cortical component to sensory re-weighting that is more distributed and requires greater attention in older adults.

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy.

Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods.

Inter-Slice Blood Flow and Magnetization Transfer Effects as A New Simultaneous Imaging Strategy.

The recent blood flow and magnetization transfer (MT) technique termed alternate ascending/descending directional navigation (ALADDIN) achieves the contrast using interslice blood flow and MT effects with no separate preparation RF pulse, thereby potentially overcoming limitations of conventional methods. In this study, we examined the signal characteristics of ALADDIN as a simultaneous blood flow and MT imaging strategy, by comparing it with pseudo-continuous ASL (pCASL) and conventional MT asymmetry (MTA) methods, all of which had the same bSSFP readout. Bloch-equation simulations and experiments showed ALADDIN perfusion signals increased with flip angle, whereas MTA signals peaked at flip angle around 45°-60°. ALADDIN provided signals comparable to those of pCASL and conventional MTA methods emulating the first, second, and third prior slices of ALADDIN under the same scan conditions, suggesting ALADDIN signals to be superposition of signals from multiple labeling planes. The quantitative cerebral blood flow signals from a modified continuous ASL model overestimated the perfusion signals compared to those measured with a pulsed ASL method. Simultaneous mapping of blood flow, MTA, and MT ratio in the whole brain is feasible with ALADDIN within a clinically reasonable time, which can potentially help diagnosis of various diseases.

Investigation of inter-slice magnetization transfer effects as a new method for MTR imaging of the human brain.

We present a new method for magnetization transfer (MT) ratio imaging in the brain that requires no separate saturation pulse. Interslice MT effects that are inherent to multi-slice balanced steady-state free precession (bSSFP) imaging were controlled via an interslice delay time to generate MT-weighted (0 s delay) and reference images (5-8 s delay) for MT ratio (MTR) imaging of the brain. The effects of varying flip angle and phase encoding (PE) order were investigated experimentally in normal, healthy subjects. Values of up to ∼50% and ∼40% were observed for white and gray matter MTR. Centric PE showed larger MTR, higher SNR, and better contrast between white and gray matter than linear PE. Simulations of a two-pool model of MT agreed well with in vivo MTR values. Simulations were also used to investigate the effects of varying acquisition parameters, and the effects of varying flip angle, PE steps, and interslice delay are discussed. Lastly, we demonstrated reduced banding with a non-balanced SSFP-FID sequence and showed preliminary results of interslice MTR imaging of meningioma.

Accuracy of oxygen saturation and total hemoglobin estimates in the neonatal brain using the semi-infinite slab model for FD-NIRS data analysis.

Frequency domain near-infrared spectroscopy (FD-NIRS) is a non-invasive method for measuring optical absorption in the brain. Common data analysis procedures for FD-NIRS data assume the head is a semi-infinite, homogenous medium. This assumption introduces bias in estimates of absorption (µa ), scattering ( [Formula: see text]), tissue oxygen saturation (StO2), and total hemoglobin (HbT). Previous works have investigated the accuracy of recovered µa values under this assumption. The purpose of this study was to examine the accuracy of recovered StO2 and HbT values in FD-NIRS measurements of the neonatal brain. We used Monte Carlo methods to compute light propagation through a neonate head model in order to simulate FD-NIRS measurements at 690 nm and 830 nm. We recovered µa , [Formula: see text], StO2, and HbT using common analysis procedures that assume a semi-infinite, homogenous medium and compared the recovered values to simulated values. Additionally, we characterized the effects of curvature via simulations on homogenous spheres of varying radius. Lastly, we investigated the effects of varying amounts of extra-axial fluid. Curvature induced underestimation of µa , [Formula: see text], and HbT, but had minimal effects on StO2. For the morphologically normal neonate head model, the mean absolute percent errors (MAPE) of recovered µa values were 12% and 7% for 690 nm and 830 nm, respectively, when source-detector separation was at least 20 mm. The MAPE for recovered StO2 and HbT were 6% and 9%, respectively. Larger relative errors were observed (∼20-30%), especially as StO2 and HbT deviated from normal values. Excess CSF around the brain caused very large errors in µa , [Formula: see text], and HbT, but had little effect on StO2.

Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS.

Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3-5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5-9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present.