Systemic physiology augmented functional near-infrared spectroscopy: a powerful approach to study the embodied human brain.

In this Outlook paper, we explain why an accurate physiological interpretation of functional near-infrared spectroscopy (fNIRS) neuroimaging signals is facilitated when systemic physiological activity (e.g., cardiorespiratory and autonomic activity) is measured simultaneously by employing systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). The rationale for SPA-fNIRS is twofold: (i) SPA-fNIRS enables a more complete interpretation and understanding of the fNIRS signals measured at the head since they contain components originating from neurovascular coupling and from systemic physiological sources. The systemic physiology signals measured with SPA-fNIRS can be used for regressing out physiological confounding components in fNIRS signals. Misinterpretations can thus be minimized. (ii) SPA-fNIRS enables to study the embodied brain by linking the brain with the physiological state of the entire body, allowing novel insights into their complex interplay. We envisage the SPA-fNIRS approach will become increasingly important in the future

Human CSF movement influenced by vascular low frequency oscillations and respiration

Cerebrospinal fluid (CSF) movement through the pathways within the central nervous system is of high significance for maintaining normal brain health and function. Low frequency hemodynamics and respiration have been shown to drive CSF in humans independently. Here, we hypothesize that CSF movement may be driven simultaneously (and in synchrony) by both mechanisms and study their independent and coupled effects on CSF movement using novel neck fMRI scans. Caudad CSF movement at the fourth ventricle and hemodynamics of the major neck blood vessels (internal carotid arteries and internal jugular veins) was measured from 11 young, healthy volunteers using novel neck fMRI scans with simultaneous measurement of respiration. Two distinct models of CSF movement (1. Low-frequency hemodynamics and 2. Respiration) and possible coupling between them were investigated. We show that the dynamics of brain fluids can be assessed from the neck by studying the interrelationships between major neck blood vessels and the CSF movement in the fourth ventricle. We also demonstrate that there exists a cross-frequency coupling between these two separable mechanisms. The human CSF system can respond to multiple coupled physiological forces at the same time. This information may help inform the pathological mechanisms behind CSF movement-related disorders

Respiratory challenge MRI: practical aspects

Respiratory challenge MRI is the modification of arterial oxygen (PaO2) and/or carbon dioxide (PaCO2) concentration to induce a change in cerebral function or metabolism which is then measured by MRI. Alterations in arterial gas concentrations can lead to profound changes in cerebral haemodynamics which can be studied using a variety of MRI sequences. Whilst such experiments may provide a wealth of information, conducting them can be complex and challenging. In this paper we review the rationale for respiratory challenge MRI including the effects of oxygen and carbon dioxide on the cerebral circulation. We also discuss the planning, equipment, monitoring and techniques that have been used to undertake these experiments. We finally propose some recommendations in this evolving area for conducting these experiments to enhance data quality and comparison between techniques

A practical modification to a resting state fMRI protocol for improved characterization of cerebrovascular function

Available online 24 June 2021.Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO 2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemo- dynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural ac- tivity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting- state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO 2 pressure: a breath-hold task to induce hypercapnia (CO 2 increase) and a cued deep breathing task to induce hypocapnia (CO 2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO 2 by systematically shifting the CO 2 regressor in time to optimize the model fit. This optimization inher- ently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO 2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemody- namic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.This research was supported by the Eunice Kennedy Shriver Na- tional Institute of Child Health and Human Development of the Na- tional Institutes of Health under award number K12HD073945. The pediatric dataset and cerebral palsy dataset were collected with sup- port of National Institutes of Health award R03 HD094615–01A1. The authors would like to acknowledge Marie Wasielewski and Carson Ingo for their support in acquiring these data. K.Z. was supported by an NIH-funded training program (T32EB025766). S.M. was supported by the European Union’s Horizon 2020 research and innovation pro- gram (Marie Sk ł odowska-Curie grant agreement No. 713673), a fel- lowship from La Caixa Foundation (ID 100010434, fellowship code LCF/BQ/IN17/11620063) and C.C.G was supported by the Spanish Ministry of Economy and Competitiveness (Ramon y Cajal Fellowship, RYC-2017- 21845), the Basque Government (BERC 2018–2021 and PIBA_2019_104) and the Spanish Ministry of Science, Innovation and Universities (MICINN; PID2019–105520GB-100)

Prediction of Task-Related BOLD fMRI with Amplitude Signatures of Resting-State fMRI

Blood oxygen contrast-functional magnetic resonance imaging (fMRI) signals are a convolution of neural and vascular components. Several studies indicate that task-related (T-fMRI) or resting-state (R-fMRI) responses linearly relate to hypercapnic task responses. Based on the linearity of R-fMRI and T-fMRI with hypercapnia demonstrated by different groups using different study designs, we hypothesized that R-fMRI and T-fMRI signals are governed by a common physiological mechanism and that resting-state fluctuation of amplitude (RSFA) should be linearly related to T-fMRI responses. We tested this prediction in a group of healthy younger humans where R-fMRI, T-fMRI, and hypercapnic (breath hold, BH) task measures were obtained form the same scan session during resting state and during performance of motor and BH tasks. Within individual subjects, significant linear correlations were observed between motor and BH task responses across voxels. When averaged over the whole brain, the subject-wise correlation between the motor and BH tasks showed a similar linear relationship within the group. Likewise, a significant linear correlation was observed between motor-task activity and RSFA across voxels and subjects. The linear rest–task (R–T) relationship between motor activity and RSFA suggested that R-fMRI and T-fMRI responses are governed by similar physiological mechanisms. A practical use of the R–T relationship is its potential to estimate T-fMRI responses in special populations unable to perform tasks during fMRI scanning. Using the R–T relationship determined from the first group of 12 healthy subjects, we predicted the T-fMRI responses in a second group of 7 healthy subjects. RSFA in both the lower and higher frequency ranges robustly predicted the magnitude of T-fMRI responses at the subject and voxel levels. We propose that T-fMRI responses are reliably predictable to the voxel level in situations where only R-fMRI measures are possible, and may be useful for assessing neural activity in task non-compliant clinical populations

Understanding the contribution of neural and physiological signal variation to the low repeatability of emotion-induced BOLD responses

Previous studies have reported low repeatability of BOLD activation measures during emotion processing tasks. It is not clear, however, whether low repeatability is a result of changes in the underlying neural signal over time, or due to insufficient reliability of the acquired BOLD signal caused by noise contamination. The aim of this study was to investigate the influence of “cleaning” the BOLD signal, by correcting for physiological noise and for differences in BOLD responsiveness, on measures of repeatability. Fifteen healthy volunteers were scanned on two different occasions, performing an emotion provocation task with faces (neutral, 50% fearful, 100% fearful) followed by a breath-hold paradigm to provide a marker of BOLD responsiveness. Repeatability of signal distribution (spatial repeatability) and repeatability of signal amplitude within two regions of interest (amygdala and fusiform gyrus) were estimated by calculating the intraclass correlation coefficient (ICC). Significant repeatability of signal amplitude was only found within the right amygdala during the perception of 50% fearful faces, but disappeared when physiological noise correction was performed. Spatial repeatability was higher within the fusiform gyrus than within the amygdala, and better at the group level than at the participant level. Neither physiological noise correction, nor consideration of BOLD responsiveness, assessed through the breath-holding, increased repeatability. The findings lead to the conclusion that low repeatability of BOLD response amplitude to emotional faces is more likely to be explained by the lack of stability in the underlying neural signal than by physiological noise contamination. Furthermore, reported repeatability might be a result of repeatability of task-correlated physiological variation rather than neural activity. This means that the emotion paradigm used in this study might not be useful for studies that require the BOLD response to be a stable measure of emotional processing, for example in the context of biomarkers

Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the human connectome roject

Like all resting-state functional connectivity data, the data from the Human Connectome Project (HCP) are adversely affected by structured noise artifacts arising from head motion and physiological processes. Functional connectivity estimates (Pearson's correlation coefficients) were inflated for high-motion time points and for high-motion participants. This inflation occurred across the brain, suggesting the presence of globally distributed artifacts. The degree of inflation was further increased for connections between nearby regions compared with distant regions, suggesting the presence of distance-dependent spatially specific artifacts. We evaluated several denoising methods: censoring high-motion time points, motion regression, the FMRIB independent component analysis-based X-noiseifier (FIX), and mean grayordinate time series regression (MGTR; as a proxy for global signal regression). The results suggest that FIX denoising reduced both types of artifacts, but left substantial global artifacts behind. MGTR significantly reduced global artifacts, but left substantial spatially specific artifacts behind. Censoring high-motion time points resulted in a small reduction of distance-dependent and global artifacts, eliminating neither type. All denoising strategies left differences between high- and low-motion participants, but only MGTR substantially reduced those differences. Ultimately, functional connectivity estimates from HCP data showed spatially specific and globally distributed artifacts, and the most effective approach to address both types of motion-correlated artifacts was a combination of FIX and MGTR