Evidence of a cerebrovascular postarteriole windkessel with delayed compliance

Mapping curvature domains in human V4 using CBV-sensitive layer-fMRI at 3T

Introduction

A full understanding of how we see our world remains a fundamental research question in vision neuroscience. While topographic profiling has allowed us to identify different visual areas, the exact functional characteristics and organization of areas up in the visual hierarchy (beyond V1 & V2) is still debated. It is hypothesized that visual area V4 represents a vital intermediate stage of processing spatial and curvature information preceding object recognition. Advancements in magnetic resonance imaging hardware and acquisition techniques (e.g., non-BOLD functional MRI) now permits the capture of cortical layer-specific functional properties and organization of the human brain (including the visual system) at high precision.

Methods

Here, we use functional cerebral blood volume measures to study the modularity in how responses to contours (curvature) are organized within area V4 of the human brain. To achieve this at 3 Tesla (a clinically relevant field strength) we utilize optimized high-resolution 3D-Echo Planar Imaging (EPI) Vascular Space Occupancy (VASO) measurements.

Results

Data here provide the first evidence of curvature domains in human V4 that are consistent with previous findings from non-human primates. We show that VASO and BOLD tSNR maps for functional imaging align with high field equivalents, with robust time series of changes to visual stimuli measured across the visual cortex. V4 curvature preference maps for VASO show strong modular organization compared to BOLD imaging contrast. It is noted that BOLD has a much lower sensitivity (due to known venous vasculature weightings) and specificity to stimulus contrast. We show evidence that curvature domains persist across the cortical depth. The work advances our understanding of the role of mid-level area V4 in human processing of curvature and shape features.

Impact

Knowledge of how the functional architecture and hierarchical integration of local contours (curvature) contribute to formation of shapes can inform computational models of object recognition. Techniques described here allow for quantification of individual differences in functional architecture of mid-level visual areas to help drive a better understanding of how changes in functional brain organization relate to difference in visual perception.

Functional magnetic resonance imaging signal has sub-second temporal accuracy

Neuronal activation sequence information is essential for understanding brain functions. Extracting such timing information from blood-oxygenation-level-dependent functional magnetic resonance imaging (fMRI) signals is confounded by local cerebral vascular reactivity (CVR), which varies across brain locations. Thus, detecting neuronal synchrony as well as inferring inter-regional causal modulation using fMRI signals can be biased. Here we used fast fMRI measurements sampled at 10 Hz to measure the fMRI latency difference between visual and sensorimotor areas when participants engaged in a visuomotor task. The regional fMRI timing was calibrated by subtracting the CVR latency measured by a breath-holding task. After CVR calibration, the fMRI signal at the lateral geniculate nucleus (LGN) preceded that at the visual cortex by 496 ms, followed by the fMRI signal at the sensorimotor cortex with a latency of 464 ms. Sequential LGN, visual, and sensorimotor cortex activations were found in each participant after the CVR calibration. These inter-regional fMRI timing differences across and within participants were more closely related to the reaction time after the CVR calibration. Our results suggested the feasibility of mapping brain activity using fMRI with accuracy in hundreds of milliseconds.

Evaluation of calibrated and uncalibrated optical imaging approaches for relative cerebral oxygen metabolism measurements in awake mice

Objective. The continuous delivery of oxygen is critical to sustain brain function, and therefore, measuring brain oxygen consumption can provide vital physiological insight. In this work, we examine the impact of calibration and cerebral blood flow (CBF) measurements on the computation of the relative changes in the cerebral metabolic rate of oxygen consumption (rCMRO2) from hemoglobin-sensitive intrinsic optical imaging data. Using these data, we calculate rCMRO2, and calibrate the model using an isometabolic stimulus.Approach. We used awake head-fixed rodents to obtain hemoglobin-sensitive optical imaging data to test different calibrated and uncalibrated rCMRO2models. Hypercapnia was used for calibration and whisker stimulation was used to test the impact of calibration.Main results. We found that typical uncalibrated models can provide reasonable estimates of rCMRO2with differences as small as 7%-9% compared to their calibrated models. However, calibrated models showed lower variability and less dependence on baseline hemoglobin concentrations. Lastly, we found that supplying the model with measurements of CBF significantly reduced error and variability in rCMRO2change calculations.Significance. The effect of calibration on rCMRO2calculations remains understudied, and we systematically evaluated different rCMRO2calculation scenarios that consider including different measurement combinations. This study provides a quantitative comparison of these scenarios to evaluate trade-offs that can be vital to the design of blood oxygenation sensitive imaging experiments for rCMRO2calculation.

Modeling brain network flexibility in networks of coupled oscillators: a feasibility study

Modeling the functionality of the human brain is a major goal in neuroscience for which many powerful methodologies have been developed over the last decade. The impact of working memory and the associated brain regions on the brain dynamics is of particular interest due to their connection with many functions and malfunctions in the brain. In this context, the concept of brain flexibility has been developed for the characterization of brain functionality. We discuss emergence of brain flexibility that is commonly measured by the identification of changes in the cluster structure of co-active brain regions. We provide evidence that brain flexibility can be modeled by a system of coupled FitzHugh-Nagumo oscillators where the network structure is obtained from human brain Diffusion Tensor Imaging (DTI). Additionally, we propose a straightforward and computationally efficient alternative macroscopic measure, which is derived from the Pearson distance of functional brain matrices. This metric exhibits similarities to the established patterns of brain template flexibility that have been observed in prior investigations. Furthermore, we explore the significance of the brain's network structure and the strength of connections between network nodes or brain regions associated with working memory in the observation of patterns in networks flexibility. This work enriches our understanding of the interplay between the structure and function of dynamic brain networks and proposes a modeling strategy to study brain flexibility.

Mapping and comparing fMRI connectivity networks across species

Technical advances in neuroimaging, notably in fMRI, have allowed distributed patterns of functional connectivity to be mapped in the human brain with increasing spatiotemporal resolution. Recent years have seen a growing interest in extending this approach to rodents and non-human primates to understand the mechanism of fMRI connectivity and complement human investigations of the functional connectome. Here, we discuss current challenges and opportunities of fMRI connectivity mapping across species. We underscore the critical importance of physiologically decoding neuroimaging measures of brain (dys)connectivity via multiscale mechanistic investigations in animals. We next highlight a set of general principles governing the organization of mammalian connectivity networks across species. These include the presence of evolutionarily conserved network systems, a dominant cortical axis of functional connectivity, and a common repertoire of topographically conserved fMRI spatiotemporal modes. We finally describe emerging approaches allowing comparisons and extrapolations of fMRI connectivity findings across species. As neuroscientists gain access to increasingly sophisticated perturbational, computational and recording tools, cross-species fMRI offers novel opportunities to investigate the large-scale organization of the mammalian brain in health and disease.

Nonlinear neural patterns are revealed in high frequency functional near infrared spectroscopy analysis

Functional Near Infrared Spectroscopy (fNIRS) is a useful tool for measuring hemoglobin concentration. Linear theory of the hemodynamic response function supports low frequency analysis (<0.2 Hz). However, we hypothesized that nonlinearities, arising from the complex neurovascular interactions sustaining vasomotor tone, may be revealed in higher frequency components of fNIRS signals. To test this hypothesis, we simulated nonlinear hemodynamic models to explore how blood flow autoregulation changes may alter evoked neurovascular signals in high frequencies. Next, we analyzed experimental fNIRS data to compare neural representations between fast (0.2-0.6 Hz) and slow (<0.2 Hz) waves, demonstrating that only nonlinear representations quantified by sample entropy are distinct between these frequency bands. Finally, we performed group-level distance correlation analysis to show that the cortical distribution of activity is independent only in the nonlinear analysis of fast and slow waves. Our study highlights the importance of analyzing nonlinear higher frequency effects seen in fNIRS for a comprehensive analysis of cortical neurovascular activity. Furthermore, it motivates further exploration of the nonlinear dynamics driving regional blood flow and hemoglobin concentrations.

Physiological modeling of the BOLD signal and implications for effective connectivity: A primer

In this primer, I provide an overview of the physiological processes that contribute to the observed BOLD signal (i.e., the generative biophysical model), including their time course properties within the framework of the physiologically-informed dynamic causal modeling (P-DCM). The BOLD signal is primarily determined by the change in paramagnetic deoxygenated hemoglobin, which results from combination of changes in oxygen metabolism, and cerebral blood flow and volume. Specifically, the physiological origin of the so-called BOLD signal "transients" will be discussed, including the initial overshoot, steady-state activation and the post-stimulus undershoot. I argue that incorrect physiological assumptions in the generative model of the BOLD signal can lead to incorrect inferences pertaining to both local neuronal activity and effective connectivity between brain regions. In addition, I introduce the recent laminar BOLD signal model, which extends P-DCM to cortical depths-resolved BOLD signals, allowing for laminar neuronal activity to be determined using high-resolution fMRI data.

Temporal Mapper: Transition networks in simulated and real neural dynamics

Characterizing large-scale dynamic organization of the brain relies on both data-driven and mechanistic modeling, which demands a low versus high level of prior knowledge and assumptions about how constituents of the brain interact. However, the conceptual translation between the two is not straightforward. The present work aims to provide a bridge between data-driven and mechanistic modeling. We conceptualize brain dynamics as a complex landscape that is continuously modulated by internal and external changes. The modulation can induce transitions between one stable brain state (attractor) to another. Here, we provide a novel method-Temporal Mapper-built upon established tools from the field of topological data analysis to retrieve the network of attractor transitions from time series data alone. For theoretical validation, we use a biophysical network model to induce transitions in a controlled manner, which provides simulated time series equipped with a ground-truth attractor transition network. Our approach reconstructs the ground-truth transition network from simulated time series data better than existing time-varying approaches. For empirical relevance, we apply our approach to fMRI data gathered during a continuous multitask experiment. We found that occupancy of the high-degree nodes and cycles of the transition network was significantly associated with subjects' behavioral performance. Taken together, we provide an important first step toward integrating data-driven and mechanistic modeling of brain dynamics.

Effect of intracranial pressure on photoplethysmographic waveform in different cerebral perfusion territories: A computational study

Background: Intracranial photoplethysmography (PPG) signals can be measured from extracranial sites using wearable sensors and may enable long-term non-invasive monitoring of intracranial pressure (ICP). However, it is still unknown if ICP changes can lead to waveform changes in intracranial PPG signals.

Aim: To investigate the effect of ICP changes on the waveform of intracranial PPG signals of different cerebral perfusion territories.

Methods: Based on lump-parameter Windkessel models, we developed a computational model consisting three interactive parts: cardiocerebral artery network, ICP model, and PPG model. We simulated ICP and PPG signals of three perfusion territories [anterior, middle, and posterior cerebral arteries (ACA, MCA, and PCA), all left side] in three ages (20, 40, and 60 years) and four intracranial capacitance conditions (normal, 20% decrease, 50% decrease, and 75% decrease). We calculated following PPG waveform features: maximum, minimum, mean, amplitude, min-to-max time, pulsatility index (PI), resistive index (RI), and max-to-mean ratio (MMR).

Results: The simulated mean ICPs in normal condition were in the normal range (8.87–11.35 mm Hg), with larger PPG fluctuations in older subject and ACA/PCA territories. When intracranial capacitance decreased, the mean ICP increased above normal threshold (&gt;20 mm Hg), with significant decreases in maximum, minimum, and mean; a minor decrease in amplitude; and no consistent change in min-to-max time, PI, RI, or MMR (maximal relative difference less than 2%) for PPG signals of all perfusion territories. There were significant effects of age and territory on all waveform features except age on mean.

Conclusion: ICP values could significantly change the value-relevant (maximum, minimum, and amplitude) waveform features of PPG signals measured from different cerebral perfusion territories, with negligible effect on shape-relevant features (min-to-max time, PI, RI, and MMR). Age and measurement site could also significantly influence intracranial PPG waveform.

Stability and dynamics of a spectral graph model of brain oscillations

We explore the stability and dynamic properties of a hierarchical, linearized, and analytic spectral graph model for neural oscillations that integrates the structural wiring of the brain. Previously, we have shown that this model can accurately capture the frequency spectra and the spatial patterns of the alpha and beta frequency bands obtained from magnetoencephalography recordings without regionally varying parameters. Here, we show that this macroscopic model based on long-range excitatory connections exhibits dynamic oscillations with a frequency in the alpha band even without any oscillations implemented at the mesoscopic level. We show that depending on the parameters, the model can exhibit combinations of damped oscillations, limit cycles, or unstable oscillations. We determined bounds on model parameters that ensure stability of the oscillations simulated by the model. Finally, we estimated time-varying model parameters to capture the temporal fluctuations in magnetoencephalography activity. We show that a dynamic spectral graph modeling framework with a parsimonious set of biophysically interpretable model parameters can thereby be employed to capture oscillatory fluctuations observed in electrophysiological data in various brain states and diseases.

Modelling the depth-dependent VASO and BOLD responses in human primary visual cortex

Functional magnetic resonance imaging (fMRI) using a blood-oxygenation-level-dependent (BOLD) contrast is a common method for studying human brain function noninvasively. Gradient-echo (GRE) BOLD is highly sensitive to the blood oxygenation change in blood vessels; however, the spatial signal specificity can be degraded due to signal leakage from activated lower layers to superficial layers in depth-dependent (also called laminar or layer-specific) fMRI. Alternatively, physiological variables such as cerebral blood volume using the VAscular-Space-Occupancy (VASO) contrast have shown higher spatial specificity compared to BOLD. To better understand the physiological mechanisms such as blood volume and oxygenation changes and to interpret the measured depth-dependent responses, models are needed which reflect vascular properties at this scale. For this purpose, we extended and modified the "cortical vascular model" previously developed to predict layer-specific BOLD signal changes in human primary visual cortex to also predict a layer-specific VASO response. To evaluate the model, we compared the predictions with experimental results of simultaneous VASO and BOLD measurements in a group of healthy participants. Fitting the model to our experimental data provided an estimate of CBV change in different vascular compartments upon neural activity. We found that stimulus-evoked CBV change mainly occurs in small arterioles, capillaries, and intracortical arteries and that the contribution from venules and ICVs is smaller. Our results confirm that VASO is less susceptible to large vessel effects compared to BOLD, as blood volume changes in intracortical arteries did not substantially affect the resulting depth-dependent VASO profiles, whereas depth-dependent BOLD profiles showed a bias towards signal contributions from intracortical veins.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Self-Similar Functional Circuit Models of Arteries and Deterministic Fractal Operators: Theoretical Revelation for Biomimetic Materials

In this paper, the self-similar functional circuit models of arteries are proposed for bioinspired hemodynamic materials design. Based on the mechanical-electrical analogous method, the circuit model can be utilized to mimic the blood flow of arteries. The theoretical mechanism to quantitatively simulate realistic blood flow is developed by establishing a fractal circuit network with an infinite number of electrical components. We have found that the fractal admittance operator obtained from the minimum repeating unit of the fractal circuit can simply and directly determine the blood-flow regulation mechanism. Furthermore, according to the operator algebra, the fractal admittance operator on the aorta can be represented by Gaussian-type convolution kernel function. Similarly, the arteriolar operator can be described by Bessel-type function. Moreover, by the self-similar assembly pattern of the proposed model, biomimetic materials which contain self-similar circuits can be designed to mimic physiological or pathological states of blood flow. Studies show that the self-similar functional circuit model can efficiently describe the blood flow and provide an available and convenient structural theoretical revelation for the preparation of in vitro hemodynamic bionic materials.

Arterial blood stealing as a mechanism of negative BOLD response: From the steady-flow with nonlinear phase separation to a windkessel-based model

Blood Oxygen Level Dependent (BOLD) signal indirectly characterizes neuronal activity by measuring hemodynamic and metabolic changes in the nearby microvasculature. A deeper understanding of how localized changes in electrical, metabolic and hemodynamic factors translate into a BOLD signal is crucial for the interpretation of functional brain imaging techniques. While positive BOLD responses (PBR) are widely considered to be linked with neuronal activation, the origins of negative BOLD responses (NBR) have remained largely unknown. As NBRs are sometimes observed in close proximity of regions with PBR, a blood "stealing" effect, i.e., redirection of blood from a passive periphery to the area with high neuronal activity, has been postulated. In this study, we used the Hagen-Poiseuille equation to model hemodynamics in an idealized microvascular network that account for the particulate nature of blood and nonlinearities arising from the red blood cell (RBC) distribution (i.e., the Fåhraeus, Fåhraeus-Lindqvist and the phase separation effects). Using this detailed model, we evaluate determinants driving this "stealing" effect in a microvascular network with geometric parameters within physiological ranges. Model simulations predict that during localized cerebral blood flow (CBF) increases due to neuronal activation-hyperemic response, blood from surrounding vessels is reallocated towards the activated region. This stealing effect depended on the resistance of the microvasculature and the uneven distribution of RBCs at vessel bifurcations. A parsimonious model consisting of two-connected windkessel regions sharing a supplying artery was proposed to simulate the stealing effect with a minimum number of parameters. Comparison with the detailed model showed that the parsimonious model can reproduce the observed response for hematocrit values within the physiological range for different species. Our novel parsimonious model promise to be of use for statistical inference (top-down analysis) from direct blood flow measurements (e.g., arterial spin labeling and laser Doppler/Speckle flowmetry), and when combined with theoretical models for oxygen extraction/diffusion will help account for some types of NBRs.

Identification of Negative BOLD Responses in Epilepsy Using Windkessel Models

Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O₂ to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.

Measuring neuronal activity with diffuse correlation spectroscopy: a theoretical investigation

Significance: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS. Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales. Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes. Results: We found that neuronal cell motion induces a DCS signal variation of ∼ 10 - 5 . We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays. Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain-computer interfaces.

A Network Architecture for Bidirectional Neurovascular Coupling in Rat Whisker Barrel Cortex

Neurovascular coupling is typically considered as a master-slave relationship between the neurons and the cerebral vessels: the neurons demand energy which the vessels supply in the form of glucose and oxygen. In the recent past, both theoretical and experimental studies have suggested that the neurovascular coupling is a bidirectional system, a loop that includes a feedback signal from the vessels influencing neural firing and plasticity. An integrated model of bidirectionally connected neural network and the vascular network is hence required to understand the relationship between the informational and metabolic aspects of neural dynamics. In this study, we present a computational model of the bidirectional neurovascular system in the whisker barrel cortex and study the effect of such coupling on neural activity and plasticity as manifest in the whisker barrel map formation. In this model, a biologically plausible self-organizing network model of rate coded, dynamic neurons is nourished by a network of vessels modeled using the biophysical properties of blood vessels. The neural layer which is designed to simulate the whisker barrel cortex of rat transmits vasodilatory signals to the vessels. The feedback from the vessels is in the form of available oxygen for oxidative metabolism whose end result is the adenosine triphosphate (ATP) necessary to fuel neural firing. The model captures the effect of the feedback from the vascular network on the neuronal map formation in the whisker barrel model under normal and pathological (Hypoxia and Hypoxia-Ischemia) conditions.

Validating layer-specific VASO across species

Cerebral blood volume (CBV) has been shown to be a robust and important physiological parameter for quantitative interpretation of functional (f)MRI, capable of delivering highly localized mapping of neural activity. Indeed, with recent advances in ultra-high-field (≥7T) MRI hardware and associated sequence libraries, it has become possible to capture non-invasive CBV weighted fMRI signals across cortical layers. One of the most widely used approaches to achieve this (in humans) is through vascular-space-occupancy (VASO) fMRI. Unfortunately, the exact contrast mechanisms of layer-dependent VASO fMRI have not been validated for human fMRI and thus interpretation of such data is confounded. Here we validate the signal source of layer-dependent SS-SI VASO fMRI using multi-modal imaging in a rat model in response to neuronal activation (somatosensory cortex) and respiratory challenge (hypercapnia). In particular VASO derived CBV measures are directly compared to concurrent measures of total haemoglobin changes from high resolution intrinsic optical imaging spectroscopy (OIS). Quantified cortical layer profiling is demonstrated to be in agreement between VASO and contrast enhanced fMRI (using monocrystalline iron oxide nanoparticles, MION). Responses show high spatial localisation to layers of cortical processing independent of confounding large draining veins which can hamper BOLD fMRI studies, (depending on slice positioning). Thus, a cross species comparison is enabled using VASO as a common measure. We find increased VASO based CBV reactivity (3.1 ± 1.2 fold increase) in humans compared to rats. Together, our findings confirm that the VASO contrast is indeed a reliable estimate of layer-specific CBV changes. This validation study increases the neuronal interpretability of human layer-dependent VASO fMRI as an appropriate method in neuroscience application studies, in which the presence of large draining intracortical and pial veins limits neuroscientific inference with BOLD fMRI.

Rethinking the Conditions and Mechanism for Glymphatic Clearance

Critical studies that form the foundation of the glymphatic system and the clearance of metabolic by-products of unwanted proteins from the brain are reviewed. Concerns are raised about studying glymphatic flow in anesthetized animals and making assumptions about the whole brain based upon data collected from a cranial window on the cortex. A new model is proposed arguing that the flow of cerebral spinal fluid and parenchymal clearance in the perivascular system of unwanted proteins is regulated by circadian changes in brain temperature and blood flow at the level of the microvasculature.

Validation of diffuse correlation spectroscopy measures of critical closing pressure against transcranial Doppler ultrasound in stroke patients

SIGNIFICANCE

Intracranial pressure (ICP), variability in perfusion, and resulting ischemia are leading causes of secondary brain injury in patients treated in the neurointensive care unit. Continuous, accurate monitoring of cerebral blood flow (CBF) and ICP guide intervention and ultimately reduce morbidity and mortality. Currently, only invasive tools are used to monitor patients at high risk for intracranial hypertension.

AIM

Diffuse correlation spectroscopy (DCS), a noninvasive near-infrared optical technique, is emerging as a possible method for continuous monitoring of CBF and critical closing pressure (CrCP or zero-flow pressure), a parameter directly related to ICP.

APPROACH

We optimized DCS hardware and algorithms for the quantification of CrCP. Toward its clinical translation, we validated the DCS estimates of cerebral blood flow index (CBFi) and CrCP in ischemic stroke patients with respect to simultaneously acquired transcranial Doppler ultrasound (TCD) cerebral blood flow velocity (CBFV) and CrCP.

RESULTS

We found CrCP derived from DCS and TCD were highly linearly correlated (ipsilateral R2  =  0.77, p  =  9  ×  10  -  7; contralateral R2  =  0.83, p  =  7  ×  10  -  8). We found weaker correlations between CBFi and CBFV (ipsilateral R2  =  0.25, p  =  0.03; contralateral R2  =  0.48, p  =  1  ×  10  -  3) probably due to the different vasculature measured.

CONCLUSION

Our results suggest DCS is a valid alternative to TCD for continuous monitoring of CrCP.

Choosing an optimal wavelength to detect brain activity in functional near-infrared spectroscopy

Functional near-infrared spectroscopy (fNIRS) measures human brain function noninvasively. The optical response to oxy- and deoxy-hemoglobin concentration variations during brain activation is wavelength dependent because of the differing spectral shapes of the extinction coefficients of the two hemoglobin species. Choosing the optimal wavelength in fNIRS measurements is crucial to improving the performance of the technique. Here we report on a framework to estimate the spectral response to neural activation in a pre-defined local region. We found that the wavelength that exhibits the largest fractional change in the detected fluence with respect to the baseline value is around 830 nm.

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'.

A system identification analysis of optogenetically evoked electrocorticography and cerebral blood flow responses

OBJECTIVE

The main objective of this research was to study the coupling between neural circuits and the vascular network in the cortex of small rodents from system engineering point of view and generate a mathematical model for the dynamics of neurovascular coupling. The model was adopted to implement closed-loop blood flow control algorithms.

APPROACH

We used a combination of advanced technologies including optogenetics, electrocorticography, and optical coherence tomography to stimulate selected populations of neurons and simultaneously record induced electrocorticography and hemodynamic signals. We adopted system identification methods to analyze the acquired data and investigate the relation between optogenetic neural activation and consequential electrophysiology and blood flow responses.

MAIN RESULTS

We showed that the developed model, once trained by the acquired data, could successfully regenerate subtle spatio-temporal features of evoked electrocorticography and cerebral blood flow responses following an onset of optogenetic stimulation.

SIGNIFICANCE

The long term goal of this research is to open a new line for computational analysis of neurovascular coupling particularly in pathologies where the normal process of blood flow regulation in the central nervous system is disrupted including Alzheimer's disease.

Dynamics of the cerebral blood flow response to brief neural activity in human visual cortex

The blood oxygen-level dependent (BOLD) functional magnetic resonance imaging (fMRI) signal depends on an interplay of cerebral blood flow (CBF), oxygen metabolism, and cerebral blood volume. Despite wide usage of BOLD fMRI, it is not clear how these physiological components create the BOLD signal. Here, baseline CBF and its dynamics evoked by a brief stimulus (2 s) in human visual cortex were measured at 3T. We found a stereotypical CBF response: immediate increase, rising to a peak a few second after the stimulus, followed by a significant undershoot. The BOLD hemodynamic response function (HRF) was also measured in the same session. Strong correlations between HRF and CBF peak responses indicate that the flow responses evoked by neural activation in nearby gray matter drive the early HRF. Remarkably, peak CBF and HRF were also strongly modulated by baseline perfusion. The CBF undershoot was reliable and significantly correlated with the HRF undershoot. However, late-time dynamics of the HRF and CBF suggest that oxygen metabolism can also contribute to the HRF undershoot. Combined measurement of the CBF and HRF for brief neural activation is a useful tool to understand the temporal dynamics of neurovascular and neurometabolic coupling.

Dynamic microcirculation PIPE model for functional neuroimaging, non-neuroimaging, and coherent hemodynamics spectroscopy: blood volume and flow velocity variations, and vascular autoregulation

We present a dynamic microcirculation PIPE model for functional neuroimaging, non-neuroimaging, and coherent hemodynamics spectroscopy. The temporal evolution of the concentration and oxygen saturation of hemoglobin in tissue, comprised of the contributions from the arterioles, capillaries, and venules of microvasculature, is determined by time-resolved hemodynamic and metabolic variations in blood volume, flow velocity, and oxygen consumption with a fluid mechanics treatment. Key parameters regarding microcirculation can be assessed, including the effective blood transit times through the capillaries and the venules, and the rate constant of oxygen release from hemoglobin to tissue. The vascular autoregulation can further be quantified from the relationship between the resolved blood volume and flow velocity variations. The PIPE model shows excellent agreement with the experimental cerebral and cutaneous coherent hemodynamics spectroscopy (CHS) and fMRI-BOLD data. It further identifies the impaired cerebral autoregulation distinctively in hemodialysis patients compared to healthy subjects measured by CHS. This new dynamic microcirculation PIPE model provides a valuable tool for brain and other functional studies with hemodynamic-based techniques. It is instrumental in recovering physiological parameters from analyzing and interpreting the signals measured by hemodynamic-based neuroimaging and non-neuroimaging techniques such as functional near-infrared spectroscopy (fNIRS) and functional magnetic resonance imaging (fMRI) in response to brain activation, physiological challenges, or physical maneuvers.

A quantitative analysis of cell-specific contributions and the role of anesthetics to the neurovascular coupling

The neurovascular coupling (NVC) connects neuronal activity to hemodynamic responses in the brain. This connection is the basis for the interpretation of functional magnetic resonance imaging data. Despite the central role of this coupling, we lack detailed knowledge about cell-specific contributions and our knowledge about NVC is mainly based on animal experiments performed during anesthesia. Anesthetics are known to affect neuronal excitability, but how this affects the vessel diameters is not known. Due to the high complexity of NVC data, mathematical modeling is needed for a meaningful analysis. However, neither the relevant neuronal subtypes nor the effects of anesthetics are covered by current models. Here, we present a mathematical model including GABAergic interneurons and pyramidal neurons, as well as the effect of an anesthetic agent. The model is consistent with data from optogenetic experiments from both awake and anesthetized animals, and it correctly predicts data from experiments with different pharmacological modulators. The analysis suggests that no downstream anesthetic effects are necessary if one of the GABAergic interneuron signaling pathways include a Michaelis-Menten expression. This is the first example of a quantitative model that includes both the cell-specific contributions and the effect of an anesthetic agent on the NVC.

Coupled electrophysiological, hemodynamic, and cerebrospinal fluid oscillations in human sleep

Sleep is essential for both cognition and maintenance of healthy brain function. Slow waves in neural activity contribute to memory consolidation, whereas cerebrospinal fluid (CSF) clears metabolic waste products from the brain. Whether these two processes are related is not known. We used accelerated neuroimaging to measure physiological and neural dynamics in the human brain. We discovered a coherent pattern of oscillating electrophysiological, hemodynamic, and CSF dynamics that appears during non-rapid eye movement sleep. Neural slow waves are followed by hemodynamic oscillations, which in turn are coupled to CSF flow. These results demonstrate that the sleeping brain exhibits waves of CSF flow on a macroscopic scale, and these CSF dynamics are interlinked with neural and hemodynamic rhythms.

fMRI in Non-human Primate: A Review on Factors That Can Affect Interpretation and Dynamic Causal Modeling Application

Dynamic causal modeling (DCM)-a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation-was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics-particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).

Advances in techniques for imposing reciprocity in brain-behavior relations

To better understand human behavior, the emerging field of model-based cognitive neuroscience seeks to anchor psychological theory to the biological substrate from which behavior originates: the brain. Despite complex dynamics, many researchers in this field have demonstrated that fluctuations in brain activity can be related to fluctuations in components of cognitive models, which instantiate psychological theories. In this review, we discuss a number of approaches for relating brain activity to cognitive models, and expand on a framework for imposing reciprocity in the inference of mental operations from the combination of brain and behavioral data.

State Estimation of Hemodynamic Model for fMRI Under Confounds: SSM Method

Through hemodynamic models, the change of neuronal state can be estimated from functional magnetic resonance imaging (fMRI) signals. Usually, there are confounds in the fMRI signal, which will degrade the performance of the estimation for the neuronal state change. For the reason, this paper introduces a state-space model with confounds, from a conventional hemodynamic model. In this model, a successive state estimation method requires a state value vector, an error covariance, an innovation covariance, and a cross covariance to be re-derived. Thus, a confounds square-root cubature Kalman smoothing (CSCKS) algorithm is proposed in this paper. We use a Balloon-Windkessel model to generate simulation data and add confounds signals to evaluate the performance of the proposed algorithm. The experiment results show that when the signal-to-interference ratio is less than 21 dB, the CSCKS proposed in this paper reduced estimation error to 16%, whereas the traditional algorithm reduced it to only 73%.

Coupling between cerebral blood flow and cerebral blood volume: Contributions of different vascular compartments

A better understanding of the coupling between changes in cerebral blood flow (CBF) and cerebral blood volume (CBV) is vital for furthering our understanding of the BOLD response. The aim of this study was to measure CBF-CBV coupling in different vascular compartments during neural activation. Three haemodynamic parameters were measured during a visual stimulus. Look-Locker flow-sensitive alternating inversion recovery was used to measure changes in CBF and arterial CBV (CBVa ) using sequence parameters optimized for each contrast. Changes in total CBV (CBVtot ) were measured using a gadolinium-based contrast agent technique. Haemodynamic changes were extracted from a region of interest based on voxels that were activated in the CBF experiments. The CBF-CBVtot coupling constant αtot was measured as 0.16 ± 0.14 and the CBF-CBVa coupling constant αa was measured as 0.65 ± 0.24. Using a two-compartment model of the vasculature (arterial and venous), the change in venous CBV (CBVv ) was predicted for an assumed value of baseline arterial and venous blood volume. These results will enhance the accuracy and reliability of applications that rely on models of the BOLD response, such as calibrated BOLD.

Functional specialization of macaque premotor F5 subfields with respect to hand and mouth movements: A comparison of task and resting-state fMRI

Based on architectonic, tract-tracing or functional criteria, the rostral portion of ventral premotor cortex in the macaque monkey, also termed area F5, has been divided into several subfields. Cytoarchitectonical investigations suggest the existence of three subfields, F5c (convexity), F5p (posterior) and F5a (anterior). Electrophysiological investigations have suggested a gradual dorso-ventral transition from hand- to mouth-dominated motor fields, with F5p and ventral F5c strictly related to hand movements and mouth movements, respectively. The involvement of F5a in this respect, however, has received much less attention. Recently, data-driven resting-state fMRI approaches have also been used to examine the presence of distinct functional fields in macaque ventral premotor cortex. Although these studies have suggested several functional clusters in/near macaque F5, so far the parcellation schemes derived from these clustering methods do not completely retrieve the same level of F5 specialization as suggested by aforementioned invasive techniques. Here, using seed-based resting-state fMRI analyses, we examined the functional connectivity of different F5 seeds with key regions of the hand and face/mouth parieto-frontal-insular motor networks. In addition, we trained monkeys to perform either hand grasping or ingestive mouth movements in the scanner in order to compare resting-state with task-derived functional hand and mouth motor networks. In line with previous single-cell investigations, task-fMRI suggests involvement of F5p, dorsal F5c and F5a in the execution of hand grasping movements, while non-communicative mouth movements yielded particularly pronounced responses in ventral F5c. Corroborating with anatomical tracing data of macaque F5 subfields, seed-based resting-state fMRI suggests a transition from predominant functional correlations with the hand-motor network in F5p to mostly mouth-motor network functional correlations in ventral F5c. Dorsal F5c yielded robust functional correlations with both hand- and mouth-motor networks. In addition, the deepest part of the fundus of the inferior arcuate, corresponding to area 44, displayed a strikingly different functional connectivity profile compared to neighboring F5a, suggesting a different functional specialization for these two neighboring regions.

MRI techniques to measure arterial and venous cerebral blood volume

The measurement of cerebral blood volume (CBV) has been the topic of numerous neuroimaging studies. To date, however, most in vivo imaging approaches can only measure CBV summed over all types of blood vessels, including arterial, capillary and venous vessels in the microvasculature (i.e. total CBV or CBVtot). As different types of blood vessels have intrinsically different anatomy, function and physiology, the ability to quantify CBV in different segments of the microvascular tree may furnish information that is not obtainable from CBVtot, and may provide a more sensitive and specific measure for the underlying physiology. This review attempts to summarize major efforts in the development of MRI techniques to measure arterial (CBVa) and venous CBV (CBVv) separately. Advantages and disadvantages of each type of method are discussed. Applications of some of the methods in the investigation of flow-volume coupling in healthy brains, and in the detection of pathophysiological abnormalities in brain diseases such as arterial steno-occlusive disease, brain tumors, schizophrenia, Huntington's disease, Alzheimer's disease, and hypertension are demonstrated. We believe that the continual development of MRI approaches for the measurement of compartment-specific CBV will likely provide essential imaging tools for the advancement and refinement of our knowledge on the exquisite details of the microvasculature in healthy and diseased brains.

Dependence of the MR signal on the magnetic susceptibility of blood studied with models based on real microvascular networks

PURPOSE

The primary goal of this study was to estimate the value of β , the exponent in the power law relating changes of the transverse relaxation rate and intra-extravascular local magnetic susceptibility differences as Δ R 2 ∗ ∝ ( Δ χ ) β . The secondary objective was to evaluate any differences that might exist in the value of β obtained using a deoxyhemoglobin-weighted Δ χ distribution versus a constant Δ χ distribution assumed in earlier computations. The third objective was to estimate the value of β that is relevant for methods based on susceptibility contrast agents with a concentration of Δ χ higher than that used for BOLD fMRI calculations.

METHODS

Our recently developed model of real microvascular anatomical networks is used to extend the original simplified Monte-Carlo simulations to compute β from the first principles.

RESULTS

Our results show that β = 1 for most BOLD fMRI measurements of real vascular networks, as opposed to earlier predictions of β = 1 .5 using uniform Δ χ distributions. For perfusion or fMRI methods based on contrast agents, which generate larger values for Δ χ , β = 1 for B 0 ≤ 9.4 T, whereas at 14 T β can drop below 1 and the variation across subjects is large, indicating that a lower concentration of contrast agent with a lower value of Δ χ is desired for experiments at high B₀ .

CONCLUSION

These results improve our understanding of the relationship between R₂ ^* and the underlying microvascular properties. The findings will help to infer the cerebral metabolic rate of oxygen and cerebral blood volume from BOLD and perfusion MRI, respectively.

Cerebral blood volume changes during the BOLD post-stimulus undershoot measured with a combined normoxia/hyperoxia method

Cerebral blood flow (CBF) and blood oxygenation level dependent (BOLD) signal measurements make it possible to estimate steady-state changes in the cerebral metabolic rate of oxygen (CMRO2) with a calibrated BOLD method. However, extending this approach to measure the dynamics of CMRO2 requires an additional assumption: that deoxygenated cerebral blood volume (CBVdHb) follows CBF in a predictable way. A test-case for this assumption is the BOLD post-stimulus undershoot, for which one proposed explanation is a strong uncoupling of flow and blood volume with an elevated level of CBVdHb during the post-stimulus period compared to baseline due to slow blood volume recovery (Balloon Model). A challenge in testing this model is that CBVdHb differs from total blood volume, which can be measured with other techniques. In this study, the basic hypothesis of elevated CBVdHb during the undershoot was tested, based on the idea that the BOLD signal change when a subject switches from breathing a normoxic gas to breathing a hyperoxic gas is proportional to the absolute CBVdHb. In 19 subjects (8F), dual-echo BOLD responses were measured in primary visual cortex during a flickering radial checkerboard stimulus in normoxia, and the identical experiment was repeated in hyperoxia (50% O2/balance N2). The BOLD signal differences between normoxia and hyperoxia for the pre-stimulus baseline, stimulus, and post-stimulus periods were compared using an equivalent BOLD signal calculated from measured R2* changes to eliminate signal drifts. Relative to the pre-stimulus baseline, the average BOLD signal change from normoxia to hyperoxia was negative during the undershoot period (p = 0.0251), consistent with a reduction of CBVdHb and contrary to the prediction of the Balloon Model. Based on these results, the BOLD post-stimulus undershoot does not represent a case of strong uncoupling of CBVdHb and CBF, supporting the extension of current calibrated BOLD methods to estimate the dynamics of CMRO2.

Estimating hemodynamic stimulus and blood vessel compliance from cerebral blood flow data

Several key brain imaging modalities that are intended for retrieving information about neuronal activity in brain, the BOLD fMRI as a foremost example, rely on the assumption that elevated neuronal activity elicits spatiotemporally well localized increase of the oxygenated blood volume, which in turn can be monitored non-invasively. The details of the signaling in the neurovascular unit during hyperemia are still not completely understood, and remain a topic of active research, requiring good mathematical models that are able to couple the different aspects of the signaling event. In this work, the question of estimating the hemodynamic stimulus function from cerebral blood flow data is addressed. In the present model, the hemodynamic stimulus is a non-specific signal from the electrophysiological and metabolic complex that controls the compliance of the blood vessels, leading to a vasodilation and thereby to an increase of blood flow. The underlying model is based on earlier literature, and it is further developed in this article for the needs of the inverse problem, which is solved using hierarchical Bayesian methodology, addressing also the poorly known model parameters.

Effective Connectivity Measured Using Optogenetically Evoked Hemodynamic Signals Exhibits Topography Distinct from Resting State Functional Connectivity in the Mouse

Brain connectomics has expanded from histological assessment of axonal projection connectivity (APC) to encompass resting state functional connectivity (RS-FC). RS-FC analyses are efficient for whole-brain mapping, but attempts to explain aspects of RS-FC (e.g., interhemispheric RS-FC) based on APC have been only partially successful. Neuroimaging with hemoglobin alone lacks specificity for determining how activity in a population of cells contributes to RS-FC. Wide-field mapping of optogenetically defined connectivity could provide insights into the brain's structure-function relationship. We combined optogenetics with optical intrinsic signal imaging to create an efficient, optogenetic effective connectivity (Opto-EC) mapping assay. We examined EC patterns of excitatory neurons in awake, Thy1-ChR2 transgenic mice. These Thy1-based EC (Thy1-EC) patterns were evaluated against RS-FC over the cortex. Compared to RS-FC, Thy1-EC exhibited increased spatial specificity, reduced interhemispheric connectivity in regions with strong RS-FC, and appreciable connection strength asymmetry. Comparing the topography of Thy1-EC and RS-FC patterns to maps of APC revealed that Thy1-EC more closely resembled APC than did RS-FC. The more general method of Opto-EC mapping with hemoglobin can be determined for 100 sites in single animals in under an hour, and is amenable to other neuroimaging modalities. Opto-EC mapping represents a powerful strategy for examining evolving connectivity-related circuit plasticity.

Biophysics of BOLD fMRI investigated with animal models

The widely-used BOLD fMRI signal depends on various anatomical, physiological, and imaging parameters. Thus, it is important to examine its biophysical and physiological source in order to optimize, model and accurately interpret fMRI. Animal models have been used to investigate these issues to take systematic measurements and combine with conventional invasive approaches. Here, we reviewed and discussed multiple issues, including the echo time-dependent intravascular contribution and extravascular contributions, gradient-echo vs. spin-echo fMRI, the physiological source of BOLD fMRI, arterial vs. venous cerebral blood volume change, cerebral oxygen consumption change, and arterial oxygen saturation change. We then discuss future directions of animal fMRI and translation to human fMRI. Systematic biophysical BOLD fMRI studies provide insight into the modeling and interpretation of BOLD fMRI in animals and humans.

Hyperspectral imaging solutions for brain tissue metabolic and hemodynamic monitoring: past, current and future developments

Hyperspectral imaging (HSI) technologies have been used extensively in medical research, targeting various biological phenomena and multiple tissue types. Their high spectral resolution over a wide range of wavelengths enables acquisition of spatial information corresponding to different light-interacting biological compounds. This review focuses on the application of HSI to monitor brain tissue metabolism and hemodynamics in life sciences. Different approaches involving HSI have been investigated to assess and quantify cerebral activity, mainly focusing on: (1) mapping tissue oxygen delivery through measurement of changes in oxygenated (HbO2) and deoxygenated (HHb) hemoglobin; and (2) the assessment of the cerebral metabolic rate of oxygen (CMRO2) to estimate oxygen consumption by brain tissue. Finally, we introduce future perspectives of HSI of brain metabolism, including its potential use for imaging optical signals from molecules directly involved in cellular energy production. HSI solutions can provide remarkable insight in understanding cerebral tissue metabolism and oxygenation, aiding investigation on brain tissue physiological processes.

Functional MRI Responses to Passive, Active, and Observed Touch in Somatosensory and Insular Cortices of the Macaque Monkey

Neurophysiological data obtained in primates suggests that merely observing others' actions can modulate activity in the observer's motor cortices. In humans, it has been suggested that these multimodal vicarious responses extend well beyond the motor cortices, including somatosensory and insular brain regions, which seem to yield vicarious responses when witnessing others' actions, sensations, or emotions (Gazzola and Keysers, 2009). Despite the wealth of data with respect to shared action responses in the monkey motor system, whether the somatosensory and insular cortices also yield vicarious responses during observation of touch remains largely unknown. Using independent tactile and motor fMRI localizers, we first mapped the hand representations of two male monkeys' primary (SI) and secondary (SII) somatosensory cortices. In two subsequent visual experiments, we examined fMRI brain responses to (1) observing a conspecific's hand being touched or (2) observing a human hand grasping or mere touching an object or another human hand. Whereas functionally defined "tactile SI" and "tactile SII" showed little involvement in representing observed touch, vicarious responses for touch were found in parietal area PFG, consistent with recent observations in humans (Chan and Baker, 2015). Interestingly, a more anterior portion of SII, and posterior insular cortex, both of which responded when monkeys performed active grasping movements, also yielded visual responses during different instances of touch observation.SIGNIFICANCE STATEMENT Common coding of one's own and others' actions, sensations, and emotions seems to be widespread in the brain. Although it is currently unclear to what extent human somatosensory cortices yield vicarious responses when observing touch, even less is known about the presence of similar vicarious responses in monkey somatosensory cortex. We therefore localized monkey somatosensory hand representations using fMRI and investigated whether these regions yield vicarious responses while observing various instances of touch. Whereas "tactile SI and SII" did not elicit responses during touch observation, a more anterior portion of SII, in addition to area PFG and posterior insular cortex, all of which responded during monkeys' own grasping movements, yielded vicarious responses during observed touch.

The effects of capillary transit time heterogeneity on the BOLD signal

Neurovascular coupling mechanisms give rise to vasodilation and functional hyperemia upon neural activation, thereby altering blood oxygenation. This blood oxygenation level dependent (BOLD) contrast allows studies of activation patterns in the working human brain by functional MRI (fMRI). The BOLD-weighted fMRI signal shows characteristic transients in relation to functional activation, such as the so-called initial dip, overshoot, and post-stimulus undershoot. These transients are modulated by other physiological stimuli and in disease, but the underlying physiological mechanisms remain incompletely understood. Capillary transit time heterogeneity (CTH) has been shown to affect oxygen extraction, and hence blood oxygenation. Here, we examine how recently reported redistributions of capillary blood flow during functional activation would be expected to affect BOLD signal transients. We developed a three-compartment (hemoglobin, plasma, and tissue) model to predict the BOLD signal, incorporating the effects of dynamic changes in CTH. Our model predicts that the BOLD signal represents the superposition of a positive component resulting from increases in cerebral blood flow (CBF), and a negative component, resulting from elevated tissue metabolism and homogenization of capillary flows (reduced CTH). The model reproduces salient features of BOLD signal dynamics under conditions such as hypercapnia, hyperoxia, and caffeine intake, where both brain physiology and BOLD characteristics are altered. Neuroglial signaling and metabolism could affect CBF and capillary flow patterns differently. Further studies of neurovascular and neuro-capillary coupling mechanisms may help us relate BOLD signals to the firing of certain neuronal populations based on their respective BOLD "fingerprints."

Pulse sequences and parallel imaging for high spatiotemporal resolution MRI at ultra-high field

The SNR and CNR benefits of ultra-high field (UHF) have helped push the envelope of achievable spatial resolution in MRI. For applications based on susceptibility contrast where there is a large CNR gain, high quality sub-millimeter resolution imaging is now being routinely performed, particularly in fMRI and phase imaging/QSM. This has enabled the study of structure and function of very fine-scale structures in the brain. UHF has also helped push the spatial resolution of many other MRI applications as will be outlined in this review. However, this push in resolution comes at a cost of a large encoding burden leading to very lengthy scans. Developments in parallel imaging with controlled aliasing and the move away from 2D slice-by-slice imaging to much more SNR-efficient simultaneous multi-slice (SMS) and 3D acquisitions have helped address this issue. In particular, these developments have revolutionized the efficiency of UHF MRI to enable high spatiotemporal resolution imaging at an order of magnitude faster acquisition. In addition to describing the main approaches to these techniques, this review will also outline important key practical considerations in using these methods in practice. Furthermore, new RF pulse design to tackle the B1+ and SAR issues of UHF and the increased SAR and power requirement of SMS RF pulses will also be touched upon. Finally, an outlook into new developments of smart encoding in more dimensions, particularly through using better temporal/across-contrast encoding and reconstruction will be described. Just as controlled aliasing fully exploits spatial encoding in parallel imaging to provide large multiplicative gains in accelerations, the complimentary use of these new approaches in temporal and across-contrast encoding are expected to provide exciting opportunities for further large gains in efficiency to further push the spatiotemporal resolution of MRI.

Ultra-Slow Single-Vessel BOLD and CBV-Based fMRI Spatiotemporal Dynamics and Their Correlation with Neuronal Intracellular Calcium Signals

Functional MRI has been used to map brain activity and functional connectivity based on the strength and temporal coherence of neurovascular-coupled hemodynamic signals. Here, single-vessel fMRI reveals vessel-specific correlation patterns in both rodents and humans. In anesthetized rats, fluctuations in the vessel-specific fMRI signal are correlated with the intracellular calcium signal measured in neighboring neurons. Further, the blood-oxygen-level-dependent (BOLD) signal from individual venules and the cerebral-blood-volume signal from individual arterioles show correlations at ultra-slow (<0.1 Hz), anesthetic-modulated rhythms. These data support a model that links neuronal activity to intrinsic oscillations in the cerebral vasculature, with a spatial correlation length of ∼2 mm for arterioles. In complementary data from awake human subjects, the BOLD signal is spatially correlated among sulcus veins and specified intracortical veins of the visual cortex at similar ultra-slow rhythms. These data support the use of fMRI to resolve functional connectivity at the level of single vessels.

Simultaneous estimation of population receptive field and hemodynamic parameters from single point BOLD responses using Metropolis-Hastings sampling

We introduce a new approach to Bayesian pRF model estimation using Markov Chain Monte Carlo (MCMC) sampling for simultaneous estimation of pRF and hemodynamic parameters. To obtain high performance on commonly accessible hardware we present a novel heuristic consisting of interpolation between precomputed responses for predetermined stimuli and a large cross-section of receptive field parameters. We investigate the validity of the proposed approach with respect to MCMC convergence, tuning and biases. We compare different combinations of pRF - Compressive Spatial Summation (CSS), Dumoulin-Wandell (DW) and hemodynamic (5-parameter and 3-parameter Balloon-Windkessel) models within our framework with and without the usage of the new heuristic. We evaluate estimation consistency and log probability across models. We perform as well a comparison of one model with and without lookup table within the RStan framework using its No-U-Turn Sampler. We present accelerated computation of whole-ROI parameters for one subject. Finally, we discuss risks and limitations associated with the usage of the new heuristic as well as the means of resolving them. We found that the new algorithm is a valid sampling approach to joint pRF/hemodynamic parameter estimation and that it exhibits very high performance.

Experimentally constrained circuit model of cortical arteriole networks for understanding flow redistribution due to occlusion and neural activation

Computations are described which estimate flows in all branches of the cortical surface arteriole network from two-photon excited fluorescence (2PEF) microscopy images which provide the network topology and, in selected branches red blood cell (RBC) speeds and lumen diameters. Validation is done by comparing the flow predicted by the model with experimentally measured flows and by comparing the predicted flow redistribution in the network due to single-vessel strokes with experimental observations. The model predicts that tissue is protected from RBC flow decreases caused by multiple occlusions of surface arterioles but not penetrating arterioles. The model can also be used to study flow rerouting due to vessel dilations and constrictions.