Arterial CO2 Fluctuations Modulate Neuronal Rhythmicity: Implications for MEG and fMRI Studies of Resting-State Networks

Spatiotemporal features of neurovascular (un)coupling with stimulus-induced activity and hypercapnia challenge in cerebral cortex and olfactory bulb

Carbon dioxide (CO2) is traditionally considered as metabolic waste, yet its regulation is critical for brain function. It is well accepted that hypercapnia initiates vasodilation, but its effect on neuronal activity is less clear. Distinguishing how stimulus- and CO2-induced vasodilatory responses are (dis)associated with neuronal activity has profound clinical and experimental relevance. We used an optical method in mice to simultaneously image fluorescent calcium (Ca2+) transients from neurons and reflectometric hemodynamic signals during brief sensory stimuli (i.e., hindpaw, odor) and CO2 exposure (i.e., 5%). Stimuli-induced neuronal and hemodynamic responses swiftly increased within locally activated regions exhibiting robust neurovascular coupling. However, hypercapnia produced slower global vasodilation which was temporally uncoupled to neuronal deactivation. With trends consistent across cerebral cortex and olfactory bulb as well as data from GCaMP6f/jRGECO1a mice (i.e., green/red Ca2+ fluorescence), these results unequivocally reveal that stimuli and CO2 generate comparable vasodilatory responses but contrasting neuronal responses. In summary, observations of stimuli-induced regional neurovascular coupling and CO2-induced global neurovascular uncoupling call for careful appraisal when using CO2 in gas mixtures to affect vascular tone and/or neuronal excitability, because CO2 is both a potent vasomodulator and a neuromodulator.

From Lung to Brain: Respiration Modulates Neural and Mental Activity

Respiration protocols have been developed to manipulate mental states, including their use for therapeutic purposes. In this systematic review, we discuss evidence that respiration may play a fundamental role in coordinating neural activity, behavior, and emotion. The main findings are: (1) respiration affects the neural activity of a wide variety of regions in the brain; (2) respiration modulates different frequency ranges in the brain's dynamics; (3) different respiration protocols (spontaneous, hyperventilation, slow or resonance respiration) yield different neural and mental effects; and (4) the effects of respiration on the brain are related to concurrent modulation of biochemical (oxygen delivery, pH) and physiological (cerebral blood flow, heart rate variability) variables. We conclude that respiration may be an integral rhythm of the brain's neural activity. This provides an intimate connection of respiration with neuro-mental features like emotion. A respiratory-neuro-mental connection holds the promise for a brain-based therapeutic usage of respiration in mental disorders.

Mapping oxidative metabolism in the human brain with calibrated fMRI in health and disease

Conventional functional MRI (fMRI) with blood-oxygenation level dependent (BOLD) contrast is an important tool for mapping human brain activity non-invasively. Recent interest in quantitative fMRI has renewed the importance of oxidative neuroenergetics as reflected by cerebral metabolic rate of oxygen consumption (CMR_O2) to support brain function. Dynamic CMR_O2 mapping by calibrated fMRI require multi-modal measurements of BOLD signal along with cerebral blood flow (CBF) and/or volume (CBV). In human subjects this "calibration" is typically performed using a gas mixture containing small amounts of carbon dioxide and/or oxygen-enriched medical air, which are thought to produce changes in CBF (and CBV) and BOLD signal with minimal or no CMR_O2 changes. However non-human studies have demonstrated that the "calibration" can also be achieved without gases, revealing good agreement between CMR_O2 changes and underlying neuronal activity (e.g., multi-unit activity and local field potential). Given the simpler set-up of gas-free calibrated fMRI, there is evidence of recent clinical applications for this less intrusive direction. This up-to-date review emphasizes technological advances for such translational gas-free calibrated fMRI experiments, also covering historical progression of the calibrated fMRI field that is impacting neurological and neurodegenerative investigations of the human brain.

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.

Carbon dioxide effects on daytime sleepiness and EEG signal: A combinational approach using classical frequentist and Bayesian analyses

Environmental carbon dioxide (CO₂ ) could affect various mental and physiological activities in humans, but its effect on daytime sleepiness is still controversial. In a randomized and counterbalanced crossover study with twelve healthy volunteers, we applied a combinational approach using classical frequentist and Bayesian statistics to analyze the CO₂ exposure effect on daytime sleepiness and electroencephalogram (EEG) signals. Subjective sleepiness was measured by the Japanese Karolinska Sleepiness Scale (KSS-J) by recording EEG during CO₂ exposure at different concentrations: Normal (C), 4000 ppm (Moderately High: MH), and 40 000 ppm (high: H). The daytime sleepiness was significantly affected by the exposure time but not the CO₂ condition in the classical statistics. On the other hand, the Bayesian paired t-test revealed that the CO₂ exposure at the MH condition might induce daytime sleepiness at the 40-min point compared with the C condition. By contrast, EEG was significantly affected by a short exposure to the H condition but not exposure time. The Bayesian analysis of EEG was primarily consistent with results by the classical statistics but showed different credible levels in the Bayes' factor. Our result suggested that the EEG may not be suitable to detect objective sleepiness induced by CO₂ exposure because the EEG signal was highly sensitive to environmental CO₂ concentration. Our study would be helpful for researchers to revisit whether EEG is applicable as a judgment indicator of objective sleepiness.

Hemodynamic and metabolic changes during hypercapnia with normoxia and hyperoxia using pCASL and TRUST MRI in healthy adults

Blood oxygenation level-dependent (BOLD) or arterial spin labeling (ASL) MRI with hypercapnic stimuli allow for measuring cerebrovascular reactivity (CVR). Hypercapnic stimuli are also employed in calibrated BOLD functional MRI for quantifying neuronally-evoked changes in cerebral oxygen metabolism (CMRO₂). It is often assumed that hypercapnic stimuli (with or without hyperoxia) are iso-metabolic; increasing arterial CO₂ or O₂ does not affect CMRO₂. We evaluated the null hypothesis that two common hypercapnic stimuli, 'CO₂ in air' and carbogen, are iso-metabolic. TRUST and ASL MRI were used to measure the cerebral venous oxygenation and cerebral blood flow (CBF), from which the oxygen extraction fraction (OEF) and CMRO₂ were calculated for room-air, 'CO₂ in air' and carbogen. As expected, CBF significantly increased (9.9% ± 9.3% and 12.1% ± 8.8% for 'CO₂ in air' and carbogen, respectively). CMRO₂ decreased for 'CO₂ in air' (-13.4% ± 13.0%, p < 0.01) compared to room-air, while the CMRO₂ during carbogen did not significantly change. Our findings indicate that 'CO₂ in air' is not iso-metabolic, while carbogen appears to elicit a mixed effect; the CMRO₂ reduction during hypercapnia is mitigated when including hyperoxia. These findings can be important for interpreting measurements using hypercapnic or hypercapnic-hyperoxic (carbogen) stimuli.

Depression Classification Using Frequent Subgraph Mining Based on Pattern Growth of Frequent Edge in Functional Magnetic Resonance Imaging Uncertain Network

The brain network structure is highly uncertain due to the noise in imaging signals and evaluation methods. Recent works have shown that uncertain brain networks could capture uncertain information with regards to functional connections. Most of the existing research studies covering uncertain brain networks used graph mining methods for analysis; for example, the mining uncertain subgraph patterns (MUSE) method was used to mine frequent subgraphs and the discriminative feature selection for uncertain graph classification (DUG) method was used to select discriminant subgraphs. However, these methods led to a lack of effective discriminative information; this reduced the classification accuracy for brain diseases. Therefore, considering these problems, we propose an approximate frequent subgraph mining algorithm based on pattern growth of frequent edge (unFEPG) for uncertain brain networks and a novel discriminative feature selection method based on statistical index (dfsSI) to perform graph mining and selection. Results showed that compared with the conventional methods, the unFEPG and dfsSI methods achieved a higher classification accuracy. Furthermore, to demonstrate the efficacy of the proposed method, we used consistent discriminative subgraph patterns based on thresholding and weighting approaches to compare the classification performance of uncertain networks and certain networks in a bidirectional manner. Results showed that classification performance of the uncertain network was superior to that of the certain network within a defined sparsity range. This indicated that if a better classification performance is to be achieved, it is necessary to select a certain brain network with a higher threshold or an uncertain brain network model. Moreover, if the uncertain brain network model was selected, it is necessary to make full use of the uncertain information of its functional connection.

Keeping the Breath in Mind: Respiration, Neural Oscillations, and the Free Energy Principle

Scientific interest in the brain and body interactions has been surging in recent years. One fundamental yet underexplored aspect of brain and body interactions is the link between the respiratory and the nervous systems. In this article, we give an overview of the emerging literature on how respiration modulates neural, cognitive and emotional processes. Moreover, we present a perspective linking respiration to the free-energy principle. We frame volitional modulation of the breath as an active inference mechanism in which sensory evidence is recontextualized to alter interoceptive models. We further propose that respiration-entrained gamma oscillations may reflect the propagation of prediction errors from the sensory level up to cortical regions in order to alter higher level predictions. Accordingly, controlled breathing emerges as an easily accessible tool for emotional, cognitive, and physiological regulation.

The breathing brain: The potential of neural oscillations for the understanding of respiratory perception in health and disease

Dyspnea or breathlessness is a symptom occurring in multiple acute and chronic illnesses, however, the understanding of the neural mechanisms underlying its subjective experience is limited. In this topical review, we propose neural oscillatory dynamics and cross-frequency coupling as viable candidates for a neural mechanism underlying respiratory perception, and a technique warranting more attention in respiration research. With the evidence for the potential of neural oscillations in the study of normal and disordered breathing coming from disparate research fields with a limited history of interdisciplinary collaboration, the main objective of the review was to converge the existing research and suggest future directions. The existing findings show that distinct limbic and cortical activations, as measured by hemodynamic responses, underlie dyspnea, however, the time-scale of these activations is not well understood. The recent findings of oscillatory neural activity coupled with the respiratory rhythm could provide the solution to this problem, however, more research with a focus on dyspnea is needed. We also touch on the findings of distinct spectral patterns underlying the changes in breathing due to experimental manipulations, meditation and disease. Subsequently, we suggest general research directions and specific research designs to supplement the current knowledge using neural oscillation techniques. We argue for the benefits of interdisciplinary collaboration and the converging of neuroimaging and behavioral methods to best explain the emergence of the subjective and aversive individual experience of dyspnea.

The neuronal associations of respiratory-volume variability in the resting state

The desire to enhance the sensitivity and specificity of resting-state (rs-fMRI) measures has prompted substantial recent research into removing noise components. Chief among contributions to noise in rs-fMRI are physiological processes, and the neuronal implications of respiratory-volume variability (RVT), a main rs-fMRI-relevant physiological process, is incompletely understood. The potential implications of RVT in modulating and being modulated by autonomic nervous regulation, has yet to be fully understood by the rs-fMRI community. In this work, we use high-density electroencephalography (EEG) along with simultaneously acquired RVT recordings to help address this question. We hypothesize that (1) there is a significant relationship between EEG and RVT in multiple EEG bands, and (2) that this relationship varies by brain region. Our results confirm our first hypothesis, although all brain regions are shown to be equally implicated in RVT-related EEG-signal fluctuations. The lag between RVT and EEG is consistent with previously reported values. However, an interesting finding is related to the polarity of the correlation between RVT and EEG. Our results reveal potentially two main regimes of EEG-RVT association, one in which EEG leads RVT with a positive association between the two, and one in which RVT leads EEG but with a negative association between the two. We propose that these two patterns can be interpreted differently in terms of the involvement of higher cognition. These results further suggest that treating RVT simply as noise is likely a questionable practice, and that more work is needed to avoid discarding cognitively relevant information when performing physiological correction rs-fMRI.

Separating vascular and neuronal effects of age on fMRI BOLD signals

Accurate identification of brain function is necessary to understand the neurobiology of cognitive ageing, and thereby promote well-being across the lifespan. A common tool used to investigate neurocognitive ageing is functional magnetic resonance imaging (fMRI). However, although fMRI data are often interpreted in terms of neuronal activity, the blood oxygenation level-dependent (BOLD) signal measured by fMRI includes contributions of both vascular and neuronal factors, which change differentially with age. While some studies investigate vascular ageing factors, the results of these studies are not well known within the field of neurocognitive ageing and therefore vascular confounds in neurocognitive fMRI studies are common. Despite over 10 000 BOLD-fMRI papers on ageing, fewer than 20 have applied techniques to correct for vascular effects. However, neurovascular ageing is not only a confound in fMRI, but an important feature in its own right, to be assessed alongside measures of neuronal ageing. We review current approaches to dissociate neuronal and vascular components of BOLD-fMRI of regional activity and functional connectivity. We highlight emerging evidence that vascular mechanisms in the brain do not simply control blood flow to support the metabolic needs of neurons, but form complex neurovascular interactions that influence neuronal function in health and disease. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Rude mechanicals in brain haemodynamics: non-neural actors that influence blood flow

Fluctuations in blood oxygenation and flow are widely used to infer brain activity during resting-state functional magnetic resonance imaging (fMRI). However, there are strong systemic and vascular contributions to resting-state signals that are unrelated to ongoing neural activity. Importantly, these non-neural contributions to haemodynamic signals (or 'rude mechanicals') can be as large as or larger than the neurally evoked components. Here, we review the two broad classes of drivers of these signals. One is systemic and is tied to fluctuations in external drivers such as heart rate and breathing, and the robust autoregulatory mechanisms that try to maintain a constant milieu in the brain. The other class comprises local, active fluctuations that appear to be intrinsic to vascular tissue and are likely similar to active local fluctuations seen in vasculature all over the body. In this review, we describe these non-neural fluctuations and some of the tools developed to correct for them when interpreting fMRI recordings. However, we also emphasize the links between these vascular fluctuations and brain physiology and point to ways in which fMRI measurements can be used to exploit such links to gain valuable information about neurovascular health and about internal brain states. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.

Depth and phase of respiration modulate cortico-muscular communication

Recent studies in animals have convincingly demonstrated that respiration cyclically modulates oscillatory neural activity across diverse brain areas. To what extent this generalises to humans in a way that is relevant for behaviour is yet unclear. We used magnetoencephalography (MEG) to assess the potential influence of respiration depth and respiration phase on the human motor system. We obtained simultaneous recordings of brain activity, muscle activity, and respiration while participants performed a steady contraction task. We used corticomuscular coherence as a measure of efficient long-range cortico-peripheral communication. We found coherence within the beta range over sensorimotor cortex to be reduced during voluntary deep compared to involuntary normal breathing. Moreover, beta coherence was found to be cyclically modulated by respiration phase in both conditions. Overall, these results demonstrate how respiratory rhythms influence the synchrony of brain oscillations, conceivably regulating computational efficiency through neural excitability. Intriguing questions remain with regard to the shape of these modulatory processes and how they influence perception, cognition, and behaviour.

Vascular physiology drives functional brain networks

We present the first evidence for vascular regulation driving fMRI signals in specific functional brain networks. Using concurrent neuronal and vascular stimuli, we collected 30 BOLD fMRI datasets in 10 healthy individuals: a working memory task, flashing checkerboard stimulus, and CO2 inhalation challenge were delivered in concurrent but orthogonal paradigms. The resulting imaging data were averaged together and decomposed using independent component analysis, and three "neuronal networks" were identified as demonstrating maximum temporal correlation with the neuronal stimulus paradigms: Default Mode Network, Task Positive Network, and Visual Network. For each of these, we observed a second network component with high spatial overlap. Using dual regression in the original 30 datasets, we extracted the time-series associated with these network pairs and calculated the percent of variance explained by the neuronal or vascular stimuli using a normalized R2 parameter. In each pairing, one network was dominated by the appropriate neuronal stimulus, and the other was dominated by the vascular stimulus as represented by the end-tidal CO2 time-series recorded in each scan. We acquired a second dataset in 8 of the original participants, where no CO2 challenge was delivered and CO2 levels fluctuated naturally with breathing variations. Although splitting of functional networks was not robust in these data, performing dual regression with the network maps from the original analysis in this new dataset successfully replicated our observations. Thus, in addition to responding to localized metabolic changes, the brain's vasculature may be regulated in a coordinated manner that mimics (and potentially supports) specific functional brain networks. Multi-modal imaging and advances in fMRI acquisition and analysis could facilitate further study of the dual nature of functional brain networks. It will be critical to understand network-specific vascular function, and the behavior of a coupled vascular-neural network, in future studies of brain pathology.

The CO2 stimulus duration and steady-state time point used for data extraction alters the cerebrovascular reactivity outcome measure

NEW FINDINGS

What is the central question of this study? Cerebrovascular reactivity (CVR) is a common functional test to assess brain health, and impaired CVR has been associated with all-cause cardiovascular mortality: does the duration of the CO₂ stimulus and the time point used for data extraction alter the CVR outcome measure? What is the main finding and its importance? This study demonstrated CVR measures calculated from 1 and 2 min CO₂ stimulus durations were significantly higher than CVR calculated from a 4 min CO₂ stimulus. CVRs calculated from the first 2 min of the CO₂ stimulus were significantly higher than CVR values calculated from the final minute if the duration was ≥4 min. This study highlights the need for consistent methodological approaches.

ABSTRACT

Cerebrovascular reactivity to carbon dioxide (CVR) is a common functional test to assess brain vascular health, though conflicting age and fitness effects have been reported. Studies have used different CO₂ stimulus durations to induce CVR and extracted data from different time points for analysis. Therefore, this study examined whether these differences alter CVR and explain conflicting findings. Eighteen healthy volunteers (24 ± 5 years) inhaled CO₂ for four stimulus durations (1, 2, 4 and 5 min) of 5% CO₂ (in air) via the open-circuit Douglas bag method, in a randomized order. CVR data were derived from transcranial Doppler (TCD) measures of middle cerebral artery blood velocity (MCAv), with concurrent ventilatory sensitivity to the CO₂ stimulus ( V ̇ E , C O 2 ). Repeated measures ANOVAs compared CVR and V ̇ E , C O 2 measures between stimulus durations and steady-state time points. An effect of stimulus duration was observed (P = 0.002, η² = 0.140), with 1 min (P = 0.010) and 2 min (P < 0.001) differing from 4 min, and 2 min differing from 5 min (P = 0.019) durations. V ̇ E , C O 2 sensitivity increased ∼3-fold from 1 min to 4 and 5 min durations (P < 0.001, η² = 0.485). CVRs calculated from different steady-state time points within each stimulus duration were different (P < 0.001, η² = 0.454), specifically for 4 min (P = 0.001) and 5 min (P < 0.001), but not 2 min stimulus durations (P = 0.273). These findings demonstrate that methodological differences alter the CVR measure.

Time of day is associated with paradoxical reductions in global signal fluctuation and functional connectivity

The brain exhibits substantial diurnal variation in physiology and function, but neuroscience studies rarely report or consider the effects of time of day. Here, we examined variation in resting-state functional MRI (fMRI) in around 900 individuals scanned between 8 AM and 10 PM on two different days. Multiple studies across animals and humans have demonstrated that the brain’s global signal (GS) amplitude (henceforth referred to as “fluctuation”) increases with decreased arousal. Thus, in accord with known circadian variation in arousal, we hypothesised that GS fluctuation would be lowest in the morning, increase in the midafternoon, and dip in the early evening. Instead, we observed a cumulative decrease in GS fluctuation as the day progressed. Although respiratory variation also decreased with time of day, control analyses suggested that this did not account for the reduction in GS fluctuation. Finally, time of day was associated with marked decreases in resting-state functional connectivity across the whole brain. The magnitude of decrease was significantly stronger than associations between functional connectivity and behaviour (e.g., fluid intelligence). These findings reveal time of day effects on global brain activity that are not easily explained by expected arousal state or physiological artefacts. We conclude by discussing potential mechanisms for the observed diurnal variation in resting brain activity and the importance of accounting for time of day in future studies.

The brain exhibits substantial diurnal variation in physiology and function. A large-scale fMRI study reveals that the brain’s global signal amplitude, typically elevated during drowsy states, unexpectedly reduces steadily as the day progresses.

The association between BOLD-based cerebrovascular reactivity (CVR) and end-tidal CO2 in healthy subjects

Cerebrovascular reactivity (CVR) mapping using CO₂-inhalation can provide important insight into vascular health. At present, blood-oxygenation-level-dependent (BOLD) MRI acquisition is the most commonly used CVR method due to its high sensitivity, high spatial resolution, and relatively straightforward processing. However, large variations in CVR across subjects and across different sessions of the same subject are often observed, which can cloud the ability of this promising measure in detecting diseases or monitoring treatment responses. The present work aims to identify the physiological components underlying the observed variability in CVR data. When studying the association between CVR value and the subject’s CO₂ levels in a total of N = 253 healthy participants, we found that CVR was lower in individuals with a higher basal end-tidal CO₂, EtCO₂ (slope = −0.0036 ± 0.0008%/mmHg², p < 0.001), or with a greater EtCO₂ change (ΔEtCO₂) with hypercapnic condition (slope = −0.0072 ± 0.0018%/mmHg², p < 0.001). In a within-subject setting, when studying the CVR difference between two repeated scans (with repositioning) in relation to the corresponding differences in basal EtCO₂ and ΔEtCO₂ (n = 11), it was found that CVR values were lower if the basal EtCO₂ or ΔEtCO₂ during that particular scan session was greater. The present work suggests that basal physiological state and the level of hypercapnic stimulus intensity should be considered in application studies of CVR in order to reduce inter-subject and intra-subject variations in the data. Potential approaches to use these findings to reduce noise and augment sensitivity are proposed.

Identification of physiological response functions to correct for fluctuations in resting-state fMRI related to heart rate and respiration

Functional magnetic resonance imaging (fMRI) is widely viewed as the gold standard for studying brain function due to its high spatial resolution and non-invasive nature. However, it is well established that changes in breathing patterns and heart rate strongly influence the blood oxygen-level dependent (BOLD) fMRI signal and this, in turn, can have considerable effects on fMRI studies, particularly resting-state studies. The dynamic effects of physiological processes are often quantified by using convolution models along with simultaneously recorded physiological data. In this context, physiological response function (PRF) curves (cardiac and respiratory response functions), which are convolved with the corresponding physiological fluctuations, are commonly employed. While it has often been suggested that the PRF curves may be region- or subject-specific, it is still an open question whether this is the case. In the present study, we propose a novel framework for the robust estimation of PRF curves and use this framework to rigorously examine the implications of using population-, subject-, session- and scan-specific PRF curves. The proposed framework was tested on resting-state fMRI and physiological data from the Human Connectome Project. Our results suggest that PRF curves vary significantly across subjects and, to a lesser extent, across sessions from the same subject. These differences can be partly attributed to physiological variables such as the mean and variance of the heart rate during the scan. The proposed methodological framework can be used to obtain robust scan-specific PRF curves from data records with duration longer than 5 min, exhibiting significantly improved performance compared to previously defined canonical cardiac and respiration response functions. Besides removing physiological confounds from the BOLD signal, accurate modeling of subject- (or session-/scan-) specific PRF curves is of importance in studies that involve populations with altered vascular responses, such as aging subjects.

Cerebrovascular reactivity (CVR) MRI with CO2 challenge: A technical review

Cerebrovascular reactivity (CVR) is an indicator of cerebrovascular reserve and provides important information about vascular health in a range of brain conditions and diseases. Unlike steady-state vascular parameters, such as cerebral blood flow (CBF) and cerebral blood volume (CBV), CVR measures the ability of cerebral vessels to dilate or constrict in response to challenges or maneuvers. Therefore, CVR mapping requires a physiological challenge while monitoring the corresponding hemodynamic changes in the brain. The present review primarily focuses on methods that use CO2 inhalation as a physiological challenge while monitoring changes in hemodynamic MRI signals. CO2 inhalation has been increasingly used in CVR mapping in recent literature due to its potency in causing vasodilation, rapid onset and cessation of the effect, as well as advances in MRI-compatible gas delivery apparatus. In this review, we first discuss the physiological basis of CVR mapping using CO2 inhalation. We then review the methodological aspects of CVR mapping, including gas delivery apparatus, the timing paradigm of the breathing challenge, the MRI imaging sequence, and data analysis. In addition, we review alternative approaches for CVR mapping that do not require CO2 inhalation.

Analysis of generic coupling between EEG activity and PETCO2 in free breathing and breath-hold tasks using Maximal Information Coefficient (MIC)

Brain activations related to the control of breathing are not completely known. The respiratory system is a non-linear system. However, the relationship between neural and respiratory dynamics is usually estimated through linear correlation measures, completely neglecting possible underlying nonlinear interactions. This study evaluate the linear and nonlinear coupling between electroencephalographic (EEG) signal and variations in carbon dioxide (CO₂) signal related to different breathing task. During a free breathing and a voluntary breath hold tasks, the coupling between EEG power in nine different brain regions in delta (1–3 Hz) and alpha (8–13 Hz) bands and end-tidal CO₂ (P_ET CO₂) was evaluated. Specifically, the generic associations (i.e. linear and nonlinear correlations) and a “pure” nonlinear correlations were evaluated using the maximum information coefficient (MIC) and MIC-ρ² between the two signals, respectively (where ρ² represents the Pearson’s correlation coefficient). Our results show that in delta band, MIC indexes discriminate the two tasks in several regions, while in alpha band the same behaviour is observed for MIC-ρ², suggesting a generic coupling between delta EEG power and P_ETCO₂ and a pure nonlinear interaction between alpha EEG power and P_ETCO₂. Moreover, higher indexes values were found for breath hold task respect to free breathing.

Multiparametric measurement of cerebral physiology using calibrated fMRI

The ultimate goal of calibrated fMRI is the quantitative imaging of oxygen metabolism (CMRO₂), and this has been the focus of numerous methods and approaches. However, one underappreciated aspect of this quest is that in the drive to measure CMRO₂, many other physiological parameters of interest are often acquired along the way. This can significantly increase the value of the dataset, providing greater information that is clinically relevant, or detail that can disambiguate the cause of signal variations. This can also be somewhat of a double-edged sword: calibrated fMRI experiments combine multiple parameters into a physiological model that requires multiple steps, thereby providing more opportunity for error propagation and increasing the noise and error of the final derived values. As with all measurements, there is a trade-off between imaging time, spatial resolution, coverage, and accuracy. In this review, we provide a brief overview of the benefits and pitfalls of extracting multiparametric measurements of cerebral physiology through calibrated fMRI experiments.

Graded Hypercapnia-Calibrated BOLD: Beyond the Iso-metabolic Hypercapnic Assumption

Calibrated BOLD is a promising technique that overcomes the sensitivity of conventional fMRI to the cerebrovascular state; measuring either the basal level, or the task-induced response of cerebral metabolic rate of oxygen consumption (CMRO₂). The calibrated BOLD method is susceptible to errors in the measurement of the calibration parameter M, the theoretical BOLD signal change that would occur if all deoxygenated hemoglobin were removed. The original and most popular method for measuring M uses hypercapnia (an increase in arterial CO₂), making the assumption that it does not affect CMRO₂. This assumption has since been challenged and recent studies have used a corrective term, based on literature values of a reduction in basal CMRO₂ with hypercapnia. This is not ideal, as this value may vary across subjects and regions of the brain, and will depend on the level of hypercapnia achieved. Here we propose a new approach, using a graded hypercapnia design and the assumption that CMRO₂ changes linearly with hypercapnia level, such that we can measure M without assuming prior knowledge of the scale of CMRO₂ change. Through use of a graded hypercapnia gas challenge, we are able to remove the bias caused by a reduction in basal CMRO₂ during hypercapnia, whilst simultaneously calculating the dose-wise CMRO₂ change with hypercapnia. When compared with assuming no change in CMRO₂, this approach resulted in significantly lower M-values in both visual and motor cortices, arising from significant dose-dependent hypercapnia reductions in basal CMRO₂ of 1.5 ± 0.6%/mmHg (visual) and 1.8 ± 0.7%/mmHg (motor), where mmHg is the unit change in end-tidal CO₂ level. Variability in the basal CMRO₂ response to hypercapnia, due to experimental differences and inter-subject variability, is accounted for in this approach, unlike previous correction approaches, which use literature values. By incorporating measurement of, and correction for, the reduction in basal CMRO₂ during hypercapnia in the measurement of M-values, application of our approach will correct for an overestimation in both CMRO₂ task-response values and absolute CMRO₂.

The impact of hyperoxia on brain activity: A resting-state and task-evoked electroencephalography (EEG) study

A better understanding of the effect of oxygen on brain electrophysiological activity may provide a more mechanistic insight into clinical studies that use oxygen treatment in pathological conditions, as well as in studies that use oxygen to calibrate functional magnetic resonance imaging (fMRI) signals. This study applied electroencephalography (EEG) in healthy subjects and investigated how high a concentration of oxygen in inhaled air (i.e., normobaric hyperoxia) alters brain activity under resting-state and task-evoked conditions. Study 1 investigated its impact on resting EEG and revealed that hyperoxia suppressed α (8-13Hz) and β (14-35Hz) band power (by 15.6±2.3% and 14.1±3.1%, respectively), but did not change the δ (1-3Hz), θ (4-7Hz), and γ (36-75Hz) bands. Sham control experiments did not result in such changes. Study 2 reproduced these findings, and, furthermore, examined the effect of hyperoxia on visual stimulation event-related potentials (ERP). It was found that the main peaks of visual ERP, specifically N1 and P2, were both delayed during hyperoxia compared to normoxia (P = 0.04 and 0.02, respectively). In contrast, the amplitude of the peaks did not show a change. Our results suggest that hyperoxia has a pronounced effect on brain neural activity, for both resting-state and task-evoked potentials.

Multimodal brain imaging with magnetoencephalography: A method for measuring blood pressure and cardiorespiratory oscillations

Studies with magnetoencephalography (MEG) are still quite rarely combined simultaneously with methods that can provide a metabolic dimension to MEG investigations. In addition, continuous blood pressure measurements which comply with MEG compatibility requirements are lacking. For instance, by combining methods reflecting neurovascular status one could obtain more information on low frequency fluctuations that have recently gained increasing interest as a mediator of functional connectivity within brain networks. This paper presents a multimodal brain imaging setup, capable to non-invasively and continuously measure cerebral hemodynamic, cardiorespiratory and blood pressure oscillations simultaneously with MEG. In the setup, all methods apart from MEG rely on the use of fibre optics. In particular, we present a method for measuring of blood pressure and cardiorespiratory oscillations continuously with MEG. The potential of this type of multimodal setup for brain research is demonstrated by our preliminary studies on human, showing effects of mild hypercapnia, gathered simultaneously with the presented modalities.

Potentiation of motor sub-networks for motor control but not working memory: Interaction of dACC and SMA revealed by resting-state directed functional connectivity

The dorsal Anterior Cingulate Cortex (dACC) and the Supplementary Motor Area (SMA) are known to interact during motor coordination behavior. We previously discovered that the directional influences underlying this interaction in a visuo-motor coordination task are asymmetric, with the dACC→SMA influence being significantly greater than that in the reverse direction. To assess the specificity of this effect, here we undertook an analysis of the interaction between dACC and SMA in two distinct contexts. In addition to the motor coordination task, we also assessed these effects during a (n-back) working memory task. We applied directed functional connectivity analysis to these two task paradigms, and also to the rest condition of each paradigm, in which rest blocks were interspersed with task blocks. We report here that the previously known asymmetric interaction between dACC and SMA, with dACC→SMA dominating, was significantly larger in the motor coordination task than the memory task. Moreover the asymmetry between dACC and SMA was reversed during the rest condition of the motor coordination task, but not of the working memory task. In sum, the dACC→SMA influence was significantly greater in the motor task than the memory task condition, and the SMA→dACC influence was significantly greater in the motor rest than the memory rest condition. We interpret these results as suggesting that the potentiation of motor sub-networks during the motor rest condition supports the motor control of SMA by dACC during the active motor task condition.

Towards a consensus regarding global signal regression for resting state functional connectivity MRI

The number of resting state functional connectivity MRI studies continues to expand at a rapid rate along with the options for data processing. Of the processing options, few have generated as much controversy as global signal regression and the subsequent observation of negative correlations (anti-correlations). This debate has motivated new processing strategies and advancement in the field, but has also generated significant confusion and contradictory guidelines. In this article, we work towards a consensus regarding global signal regression. We highlight several points of agreement including the fact that there is not a single "right" way to process resting state data that reveals the "true" nature of the brain. Although further work is needed, different processing approaches likely reveal complementary insights about the brain's functional organisation.