Echo-time dependence of the BOLD response transients – A window into brain functional physiology

On the influence of the vascular architecture on Gradient Echo and Spin Echo BOLD fMRI signals across cortical depth: a simulation approach based on realistic 3D vascular networks

GE-BOLD contrast stands out as the predominant technique in functional MRI experiments for its high sensitivity and straightforward implementation. GE-BOLD exhibits rather similar sensitivity to vessels independent of their size at submillimeter resolution studies like those examining cortical columns and laminae. However, the presence of nonspecific macrovascular contributions poses a challenge to accurately isolate neuronal activity. SE-BOLD increases specificity towards small vessels, thereby enhancing its specificity to neuronal activity, due to the effective suppression of extravascular contributions caused by macrovessels with its refocusing pulse. However, even SE-BOLD measurements may not completely remove these macrovascular contributions. By simulating hemodynamic signals across cortical depth, we gain insights into vascular contributions to the laminar BOLD signal. In this study, we employed four realistic 3D vascular models to simulate oxygen saturation states in various vascular compartments, aiming to characterize both intravascular and extravascular contributions to GE and SE signals, and corresponding BOLD signal changes, across cortical depth at 7T. Simulations suggest that SE-BOLD cannot completely reduce the macrovascular contribution near the pial surface. Simulations also show that both the specificity and signal amplitude of BOLD signals at 7T depend on the spatial arrangement of large vessels throughout cortical depth and on the pial surface.

A fully synthetic three-dimensional human cerebrovascular model based on histological characteristics to investigate the hemodynamic fingerprint of the layer BOLD fMRI signal formation

Recent advances in functional magnetic resonance imaging (fMRI) at ultra-high field (≥7 tesla), novel hardware, and data analysis methods have enabled detailed research on neurovascular function, such as cortical layer-specific activity, in both human and nonhuman species. A widely used fMRI technique relies on the blood oxygen level-dependent (BOLD) signal. BOLD fMRI offers insights into brain function by measuring local changes in cerebral blood volume, cerebral blood flow, and oxygen metabolism induced by increased neuronal activity. Despite its potential, interpreting BOLD fMRI data is challenging as it is only an indirect measurement of neuronal activity. Computational modeling can help interpret BOLD data by simulating the BOLD signal formation. Current developments have focused on realistic 3D vascular models based on rodent data to understand the spatial and temporal BOLD characteristics. While such rodent-based vascular models highlight the impact of the angioarchitecture on the BOLD signal amplitude, anatomical differences between the rodent and human vasculature necessitate the development of human-specific models. Therefore, a computational framework integrating human cortical vasculature, hemodynamic changes, and biophysical properties is essential. Here, we present a novel computational approach: a three-dimensional VAscular MOdel based on Statistics (3D VAMOS), enabling the investigation of the hemodynamic fingerprint of the BOLD signal within a model encompassing a fully synthetic human 3D cortical vasculature and hemodynamics. Our algorithm generates microvascular and macrovascular architectures based on morphological and topological features from the literature on human cortical vasculature. By simulating specific oxygen saturation states and biophysical interactions, our framework characterizes the intravascular and extravascular signal contributions across cortical depth and voxel-wise levels for gradient-echo and spin-echo readouts. Thereby, the 3D VAMOS computational framework demonstrates that using human characteristics significantly affects the BOLD fingerprint, making it an essential step in understanding the fundamental underpinnings of layer-specific fMRI experiments.

A mechanistic computational framework to investigate the hemodynamic fingerprint of the blood oxygenation level-dependent signal

Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is one of the most used imaging techniques to map brain activity or to obtain clinical information about human cortical vasculature, in both healthy and disease conditions. Nevertheless, BOLD fMRI is an indirect measurement of brain functioning triggered by neurovascular coupling. The origin of the BOLD signal is quite complex, and the signal formation thus depends, among other factors, on the topology of the cortical vasculature and the associated hemodynamic changes. To understand the hemodynamic evolution of the BOLD signal response in humans, it is beneficial to have a computational framework available that virtually resembles the human cortical vasculature, and simulates hemodynamic changes and corresponding MRI signal changes via interactions of intrinsic biophysical and magnetic properties of the tissues. To this end, we have developed a mechanistic computational framework that simulates the hemodynamic fingerprint of the BOLD signal based on a statistically defined, three-dimensional, vascular model that approaches the human cortical vascular architecture. The microvasculature is approximated through a Voronoi tessellation method and the macrovasculature is adapted from two-photon microscopy mice data. Using this computational framework, we simulated hemodynamic changes-cerebral blood flow, cerebral blood volume, and blood oxygen saturation-induced by virtual arterial dilation. Then we computed local magnetic field disturbances generated by the vascular topology and the corresponding blood oxygen saturation changes. This mechanistic computational framework also considers the intrinsic biophysical and magnetic properties of nearby tissue, such as water diffusion and relaxation properties, resulting in a dynamic BOLD signal response. The proposed mechanistic computational framework provides an integrated biophysical model that can offer better insights regarding the spatial and temporal properties of the BOLD signal changes.

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.

Improved brain stimulation targeting by optimising image acquisition parameters

Functional connectivity analysis from rs-fMRI data has been used for determining cortical targets in therapeutic applications of non-invasive brain stimulation using transcranial magnetic stimulation (TMS). Reliable connectivity measures are therefore essential for every rs-fMRI-based TMS targeting approach. Here, we examine the effect of echo time (TE) on the reproducibility and spatial variability of resting-state connectivity measures. We acquired multiple runs of single-echo fMRI data with either short (TE = 30 ms) or long (TE = 38 ms) echo time to investigate inter-run spatial reproducibility of a clinically relevant functional connectivity map, i.e., originating from the sgACC. We find that connectivity maps obtained from TE = 38 ms rs-fMRI data are significantly more reliable than those obtained from TE = 30 ms data sets. Our results clearly show that optimizing sequence parameters can be beneficial for ensuring high-reliability resting-state acquisition protocols to be used for TMS targeting. The differences between reliability in connectivity measures for different TEs could inform future clinical research in optimising MR sequences.

Magnetic resonance imaging at 9.4 T: the Maastricht journey

The 9.4 T scanner in Maastricht is a whole-body magnet with head gradients and parallel RF transmit capability. At the time of the design, it was conceptualized to be one of the best fMRI scanners in the world, but it has also been used for anatomical and diffusion imaging. 9.4 T offers increases in sensitivity and contrast, but the technical ultra-high field (UHF) challenges, such as field inhomogeneities and constraints set by RF power deposition, are exacerbated compared to 7 T. This article reviews some of the 9.4 T work done in Maastricht. Functional imaging experiments included blood oxygenation level-dependent (BOLD) and blood-volume weighted (VASO) fMRI using different readouts. BOLD benefits from shorter T₂* at 9.4 T while VASO from longer T₁. We show examples of both ex vivo and in vivo anatomical imaging. For many applications, pTx and optimized coils are essential to harness the full potential of 9.4 T. Our experience shows that, while considerable effort was required compared to our 7 T scanner, we could obtain high-quality anatomical and functional data, which illustrates the potential of MR acquisitions at even higher field strengths. The practical challenges of working with a relatively unique system are also discussed.

Dynamic Effective Connectivity using Physiologically informed Dynamic Causal Model with Recurrent Units: A functional Magnetic Resonance Imaging simulation study

Functional MRI (fMRI) is an indirect reflection of neuronal activity. Using generative biophysical model of fMRI data such as Dynamic Causal Model (DCM), the underlying neuronal activities of different brain areas and their causal interactions (i.e., effective connectivity) can be calculated. Most DCM studies typically consider the effective connectivity to be static for a cognitive task within an experimental run. However, changes in experimental conditions during complex tasks such as movie-watching might result in temporal variations in the connectivity strengths. In this fMRI simulation study, we leverage state-of-the-art Physiologically informed DCM (P-DCM) along with a recurrent window approach and discretization of the equations to infer the underlying neuronal dynamics and concurrently the dynamic (time-varying) effective connectivities between various brain regions for task-based fMRI. Results from simulation studies on 3- and 10-region models showed that functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) responses and effective connectivity time-courses can be accurately predicted and distinguished from faulty graphical connectivity models representing cognitive hypotheses. In summary, we propose and validate a novel approach to determine dynamic effective connectivity between brain areas during complex cognitive tasks by combining P-DCM with recurrent units.

Neurovascular coupling in patients with type 2 diabetes mellitus

Functional and metabolic neural changes in Type 2 diabetes mellitus (T2DM) can be associated with poor cognitive performances. Here we analyzed the functional-metabolic neurovascular coupling (NVC) in the brain of T2DM patients. Thirty-three patients (70 ± 6 years, 15 males) with recent T2DM diagnosis and 18 healthy control (HC) subjects (65 ± 9 years, 9 males) were enrolled in a brain MRI study to identify the potential effects of T2DM on NVC. T2DM patients were either drug-naive (n = 19) or under treatment with metformin (n = 14) since less than 6 months. Arterial spin labeling and blood oxygen level dependent resting-state functional MRI (RS-fMRI) images were combined to derive NVC measures in brain regions and large-scale networks in a standard brain parcelation. Altered NVC values in T2DM patients were correlated with cognitive performances spanning several neurological domains using Spearman correlation coefficients. Compared to HC, T2DM patients had reduced NVC in the default mode network (DMN) and increased NVC in three regions of the dorsal (DAN) and salience-ventral (SVAN) attention networks. NVC abnormalities in DAN and SVAN were associated with reduced visuo-spatial cognitive performances. A spatial pattern of NVC reduction in the DMN, accompanied by isolated regional NVC increases in DAN and SVAN, could reflect the emergence of (defective) compensatory processes in T2DM patients in response to altered neurovascular conditions. Overall, this pattern is reminiscent of neural abnormalities previously observed in Alzheimer’s disease, suggesting that similar neurobiological mechanisms, secondary to insulin resistance and manifesting as NVC alterations, might be developing in T2DM pathology.

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.

Effects of mild hypoxia on oxygen extraction fraction responses to brain stimulation

Characterizing the effect of limited oxygen availability on brain metabolism during brain activation is an essential step towards a better understanding of brain homeostasis and has obvious clinical implications. However, how the cerebral oxygen extraction fraction (OEF) depends on oxygen availability during brain activation remains unclear, which is mostly attributable to the scarcity and safety of measurement techniques. Recently, a magnetic resonance imaging (MRI) method that enables noninvasive and dynamic measurement of the OEF has been developed and confirmed to be applicable to functional MRI studies. Using this novel method, the present study investigated the motor-evoked OEF response in both normoxia (21% O₂) and hypoxia (12% O₂). Our results showed that OEF activation decreased in the brain areas involved in motor task execution. Decreases in the motor-evoked OEF response were greater under hypoxia (-21.7% ± 5.5%) than under normoxia (-11.8% ± 3.7%) and showed a substantial decrease as a function of arterial oxygen saturation. These findings suggest a different relationship between oxygen delivery and consumption during hypoxia compared to normoxia. This methodology may provide a new perspective on the effects of mild hypoxia on brain function.

Echo Time Dependency of Local Activity Metrics of Resting-State Functional MRI

Local activity metrics of resting-state functional MRI (RS-fMRI), such as the amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo), and degree centrality (DC), are widely used to detect brain abnormalities based on signal fluctuations. Although signal changes with echo time (TE) have been widely studied, the effect of TE on local activity metrics has not been investigated. RS-fMRI datasets from 12 healthy subjects with eyes open (EO) and eyes closed (EC) were obtained with a four-echo gradient-echo-planar imaging pulse sequence with the following parameters: repetition time/TE1/TE2/TE3/TE4 = 2,000/13/30.93/48.86/66.79 ms. Six representative regions were selected for simulating the spatial feature of TE dependency of local activity metrics. Moreover, whole-brain local activity metrics were calculated from each echo dataset and compared between EO and EC conditions. Dice overlap coefficient (DOC) was then employed to calculate the overlap between the T maps. We found that all the local activity metrics displayed different TE dependency characteristics, while their overall change patterns were similar: an initial large change followed by a slow variation. The T maps for local activity metrics also varied greatly with TE. For ALFF, fALFF, ReHo, and DC, the DOCs for voxels in four TE datasets were 6.87, 0.73, 5.08, and 0.93%, respectively. Collectively, these findings demonstrate that local metrics are greatly dependent on TE. Therefore, TE should be carefully considered for the optimization of data acquisition and multi-center data analysis in RS-fMRI.

Modeling of vascular space occupancy and BOLD functional MRI from first principles using real microvascular angiograms

PURPOSE

The vascular space occupancy (VASO) is a functional MRI technique for probing cerebral blood volume changes noninvasively, including during neuronal activation in humans. An important consideration when implementing VASO is the BOLD effect in the signal. Assessing the physical origin of this BOLD contamination and the capabilities of correction methods could improve the quantification of cerebral blood volume changes with VASO.

METHODS

Given the heterogeneity of cerebral microvascular architecture, the vascular geometry within an MRI voxel can influence both BOLD and VASO signals. To investigate this effect, 3D high-resolution images of mouse cerebral vasculature measured with two-photon microscopy were used to model BOLD and VASO signals from first principles using Monte Carlo diffusion of water protons. Quantitative plots of VASO together with intravascular and extravascular BOLD signals as a function of TE at B₀ fields 1.5 T to 14 T were obtained.

RESULTS

The BOLD contamination of the VASO response was on the order of 50% for gradient echo and 5% for spin echo at 7 T and TE = 6 ms and significantly increased with TE and B₀ . Two currently used correction schemes were shown to account for most of this contamination and recover accurate relative signal changes, with optimal correction obtained using TEs as short as possible.

CONCLUSION

These results may provide useful information for optimizing sequence parameters in VASO and BOLD functional MRI, leading the way to a wider application of these techniques in healthy and diseased brain.

A deconvolution algorithm for multi-echo functional MRI: Multi-echo Sparse Paradigm Free Mapping

This work introduces a novel algorithm for deconvolution of the BOLD signal in multi-echo fMRI data: Multi-echo Sparse Paradigm Free Mapping (ME-SPFM). Assuming a linear dependence of the BOLD percent signal change on the echo time (TE) and using sparsity-promoting regularized least squares estimation, ME-SPFM yields voxelwise time-varying estimates of the changes in the apparent transverse relaxation (ΔR₂^⁎) without prior knowledge of the timings of individual BOLD events. Our results in multi-echo fMRI data collected during a multi-task event-related paradigm at 3 Tesla demonstrate that the maps of R₂^⁎ changes obtained with ME-SPFM at the times of the stimulus trials show high spatial and temporal concordance with the activation maps and BOLD signals obtained with standard model-based analysis. This method yields estimates of ΔR₂^⁎ having physiologically plausible values. Owing to its ability to blindly detect events, ME-SPFM also enables us to map ΔR₂^⁎ associated with spontaneous, transient BOLD responses occurring between trials. This framework is a step towards deciphering the dynamic nature of brain activity in naturalistic paradigms, resting-state or experimental paradigms with unknown timing of the BOLD events.

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.

Simulating laminar neuroimaging data for a visual delayed match-to-sample task

Invasive electrophysiological and neuroanatomical studies in nonhuman mammalian experimental preparations have helped elucidate the lamina (layer) dependence of neural computations and interregional connections. Noninvasive functional neuroimaging can, in principle, resolve cortical laminae (layers), and thus provide insight into human neural computations and interregional connections. However human neuroimaging data are noisy and difficult to interpret; biologically realistic simulations can aid experimental interpretation by relating the neuroimaging data to simulated neural activity. We illustrate the potential of laminar neuroimaging by upgrading an existing large-scale, multiregion neural model that simulates a visual delayed match-to-sample (DMS) task. The new laminar-based neural unit incorporates spiny stellate, pyramidal, and inhibitory neural populations which are divided among supragranular, granular, and infragranular laminae (layers). We simulated neural activity which is translated into local field potential-like data used to simulate conventional and laminar fMRI activity. We implemented the laminar connectivity schemes proposed by Felleman and Van Essen (Cerebral Cortex, 1991) for interregional connections. The hemodynamic model that we employ is a modified version of one due to Heinzle et al. (Neuroimage, 2016) that incorporates the effects of draining veins. We show that the laminar version of the model replicates the findings of the existing model. The laminar model shows the finer structure in fMRI activity and functional connectivity. Laminar differences in the magnitude of neural activities are a prominent finding; these are also visible in the simulated fMRI. We illustrate differences between task and control conditions in the fMRI signal, and demonstrate differences in interregional laminar functional connectivity that reflect the underlying connectivity scheme. These results indicate that multi-layer computational models can aid in interpreting layer-specific fMRI, and suggest that increased use of laminar fMRI could provide unique and fundamental insights to human neuroscience.

Reduced Dynamic Coupling Between Spontaneous BOLD-CBF Fluctuations in Older Adults: A Dual-Echo pCASL Study

Measurement of the dynamic coupling between spontaneous Blood Oxygenation Level Dependent (BOLD) and cerebral blood flow (CBF) fluctuations has been recently proposed as a method to probe resting-state brain physiology. Here we investigated how the dynamic BOLD-CBF coupling during resting-state is affected by aging. Fifteen young subjects and 17 healthy elderlies were studied using a dual-echo pCASL sequence. We found that the dynamic BOLD-CBF coupling was markedly reduced in elderlies, in particular in the left supramarginal gyrus, an area known to be involved in verbal working memory and episodic memory. Moreover, correcting for temporal shift between BOLD and CBF timecourses resulted in an increased correlation of the two signals for both groups, but with a larger increase for elderlies. However, even after temporal shift correction, a significantly decreased correlation was still observed for elderlies in the left supramarginal gyrus, indicating that the age-related dynamic BOLD-CBF uncoupling in this region is more pronounced and can be only partially explained with a simple time-shift between the two signals. Interestingly, these results were observed in a group of elderlies with normal cognitive functions, suggesting that the study of dynamic BOLD-CBF coupling during resting-state is a promising technique, potentially able to provide early biomarkers of functional changes in the aging brain.

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.

Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model

Hemodynamic responses, in general, and the blood oxygenation level-dependent (BOLD) fMRI signal, in particular, provide an indirect measure of neuronal activity. There is strong evidence that the BOLD response correlates well with post-synaptic changes, induced by changes in the excitatory and inhibitory (E-I) balance between active neuronal populations. Typical BOLD responses exhibit transients, such as the early-overshoot and post-stimulus undershoot, that can be linked to transients in neuronal activity, but they can also result from vascular uncoupling between cerebral blood flow (CBF) and venous cerebral blood volume (venous CBV). Recently, we have proposed a novel generative hemodynamic model of the BOLD signal within the dynamic causal modeling framework, inspired by physiological observations, called P-DCM (Havlicek et al., 2015). We demonstrated the generative model's ability to more accurately model commonly observed neuronal and vascular transients in single regions but also effective connectivity between multiple brain areas (Havlicek et al., 2017b). In this paper, we additionally demonstrate the versatility of the generative model to jointly explain dynamic relationships between neuronal and hemodynamic physiological variables underlying the BOLD signal using multi-modal data. For this purpose, we utilized three distinct data-sets of experimentally induced responses in the primary visual areas measured in human, cat, and monkey brain, respectively: (1) CBF and BOLD responses; (2) CBF, total CBV, and BOLD responses (Jin and Kim, 2008); and (3) positive and negative neuronal and BOLD responses (Shmuel et al., 2006). By fitting the generative model to the three multi-modal experimental data-sets, we showed that the presence or absence of dynamic features in the BOLD signal is not an unambiguous indication of presence or absence of those features on the neuronal level. Nevertheless, the generative model that takes into account the dynamics of the physiological mechanisms underlying the BOLD response allowed dissociating neuronal from vascular transients and deducing excitatory and inhibitory neuronal activity time-courses from BOLD data alone and from multi-modal data.

Estimation of physiological sources of nonlinearity in blood oxygenation level-dependent contrast signals

Blood oxygenation level-dependent (BOLD) contrast appears through a variation in the transverse relaxation rate of magnetic resonance signals induced by neurovascular coupling and is known to have nonlinear characteristics along echo time (TE) due to the intra-vasculature. However, the physiological causes of this nonlinearity are unclear. We attempted to estimate the physiological information related to the nonlinearity of BOLD signals by using a two-compartment model. For this purpose, we used a multi-echo gradient-echo echo-planar imaging sequence and developed a computational method to estimate the physiological information from the TE-dependent BOLD signals. The results showed that the average chemical exchange time in the intra-vasculature varied during stimulation, which might be the essential source of the nonlinearity.