A cortical vascular model for examining the specificity of the laminar BOLD signal

Communication of perceptual predictions from the hippocampus to the deep layers of the parahippocampal cortex

Current evidence suggests that the hippocampus is essential for exploiting predictive relationships during perception. However, it remains unclear whether the hippocampus drives the communication of predictions to sensory cortex or receives prediction signals from elsewhere. We collected 7-tesla fMRI data in the medial temporal lobe (MTL) while auditory cues predicted abstract shapes. Strikingly, neural patterns evoked by predicted shapes in CA2/3, pre/parasubiculum, and the parahippocampal cortex (PHC) were negatively correlated to patterns evoked by the same shapes when actually presented. Using layer-specific analyses, we ask: In which direction are predictions communicated between the hippocampus and neocortex? Superficial layers of the MTL cortex project to the hippocampus, while the deep layers receive feedback projections. Informational connectivity analyses revealed that communication between CA2/3 and PHC was specific to the deep layers of PHC. These findings suggest that the hippocampus generates predictions through pattern completion in CA2/3 and feeds these predictions back to the neocortex.

Layer-specific spatiotemporal dynamics of feedforward and feedback in human visual object perception

Visual object perception is mediated by information flow between regions of the ventral visual stream along feedforward and feedback anatomical connections. However, feedforward and feedback signals during naturalistic vision are rapid and overlapping, complicating their identification and precise functional specification. Here we recorded human layer-specific fMRI responses to naturalistic object images in early visual cortex (EVC) and lateral occipital complex (LOC) to isolate feedforward and feedback information signals spatially by their cortical layer specific termination pattern. We combined these layer-specific fMRI responses with electroencephalography (EEG) responses for the same images to segregate feedforward and feedback signals in both time and space. Feedforward signals emerge early in the middle layers of EVC and LOC, followed by feedback signals in the superficial layer of both regions, and the deep layer of EVC. Comparing the identified dynamics in LOC to a visual deep neural network (DNN), revealed that early feedforward signals in LOC encode medium complexity features, whereas later feedback signals increase the representational format to high complexity features. Together this specifies the spatiotemporal dynamics and functional role of feedforward and feedback information flow mediating visual object perception.

Towards whole brain mapping of the haemodynamic response function

Functional magnetic resonance imaging time-series are conventionally processed by linear modelling the evoked response as the convolution of the experimental conditions with a stereotyped haemodynamic response function (HRF). However, the neural signal in response to a stimulus can vary according to task, brain region, and subject-specific conditions. Moreover, HRF shape has been suggested to carry physiological information. The BOLD signal across a range of sensorial and cognitive tasks was fitted using a sine series expansion, and modelled signals were deconvolved, thus giving rise to a task-specific deconvolved HRF (dHRF), which was characterized in terms of amplitude, latency, time-to-peak and full-width at half maximum for each task. We found that the BOLD response shape changes not only across activated regions and tasks, but also across subjects despite the age homogeneity of the cohort. Largest variabilities were observed in mean amplitude and latency across tasks and regions, while time-to-peak and full width at half maximum were relatively more consistent. Additionally, the dHRF was found to deviate from canonicity in several brain regions. Our results suggest that the choice of a standard, uniform HRF may be not optimal for all fMRI analyses and may lead to model misspecifications and statistical bias.

The laminar pattern of proprioceptive activation in human primary motor cortex

The primary motor cortex (M1) is increasingly being recognized for its vital role in proprioceptive somatosensation. However, our current understanding of proprioceptive processing at the laminar scale is limited. Empirical findings in primates and rodents suggest a pronounced role of superficial cortical layers, but the involvement of deep layers has yet to be examined in humans. Submillimeter resolution functional magnetic resonance imaging (fMRI) has emerged in recent years, paving the way for studying layer-dependent activity in humans (laminar fMRI). In the present study, laminar fMRI was employed to investigate the influence of proprioceptive somatosensation on M1 deep layer activation using passive finger movements. Significant M1 deep layer activation was observed in response to proprioceptive stimulation across 10 healthy subjects using a vascular space occupancy (VASO)-sequence at 7 T. For further validation, two additional datasets were included which were obtained using a balanced steady-state free precession sequence with ultrahigh (0.3 mm) in-plane resolution, yielding converging results. These results were interpreted in the light of previous laminar fMRI studies and the active inference account of motor control. We propose that a considerable proportion of M1 deep layer activation is due to proprioceptive influence and that deep layers of M1 constitute a key component in proprioceptive circuits.

Layer-specific BOLD effects in gradient and spin-echo acquisitions in somatosensory cortex

PURPOSE

Previous studies have shown varied BOLD signals with gradient echo (GE) across cortical depth. To interpret these variations, and understand the effects of vascular geometry and size, the magnitudes and layer distributions of GE and spin-echo (SE) BOLD functional MRI signals were compared in the somatosensory cortex of squirrel monkeys during tactile stimulation and in a resting state at high spatial resolution and high field.

METHODS

A block-design stimulation was used to identify tactile-evoked activation signals in somatosensory Areas 3b and 1. Layer-specific connectivities were calculated using resting-state data. Signal power spectra were compared by depth and pulse sequence. The measured ratios of transverse relaxation rate changes were compared with Anderson and Weiss's model.

RESULTS

SE signals showed a 26% lower percentage signal change during tactile stimulation compared with GE, along with a slower time course. SE signals remained consistent but weaker in lower layers, whereas GE signals decreased with cortical depth. This pattern extended to resting-state power spectra. Resting-state functional connectivity indicated larger connectivity between the top layers of Area 3b and Area 1 for GE, with minimal changes for SE. Comparisons with theory suggest vessel diameters ranging from 19.4 to 9 microns are responsible for BOLD effects across cortical layers at 9.4 T.

CONCLUSION

These results provide further evidence that at high field, SE BOLD signals are relatively free of contributions from sources other than microvascular changes in response to neural activity, whereas GE signals, even in the superficial layers, are not dominated by very large vessels.

Does the Cortical-Depth Dependence of the Hemodynamic Response Function Differ Between Age Groups?

Functional magnetic resonance imaging (fMRI) is a widely used tool to investigate the functional brain responses in living humans. Valid comparisons of fMRI results depend on consistency of the blood-oxygen-level-dependent (BOLD) hemodynamic response function (HRF). Although common statistical approaches assume a single HRF across the entire brain, the HRF differs across individuals, regions of the brain, and cortical depth. Here, we measure HRF properties in primary visual cortex (V1) using 7 T fMRI with ultra-high spatiotemporal resolution line-scanning (250 μm in laminar direction, sampled every 105 ms). Line-scanning allowed us to investigate age-related HRF changes as a function of cortical depth. Eleven young and eleven middle-aged healthy participants participated in the experiments. We estimated the HRFs using a smooth basis function deconvolution approach. We also compared the results with conventional resolutions. From these HRFs, we extracted properties related to response magnitude and temporal dynamics. The cortical depth dependent HRFs were similar to the HRFs extracted using conventional resolutions validating the cortical depth dependent approach. We found that the properties of the HRF in the two age groups are similar across cortical depth. In other words, the variance between participants is larger than the variance between age groups. This suggests that middle-aged individuals can participate in cortical depth dependent studies free of bias in HRF properties.

Predictive acoustical processing in human cortical layers

In our dynamic environments, predictive processing is vital for auditory perception and its associated behaviors. Predictive coding formalizes inferential processes by implementing them as information exchange across cortical layers and areas. With laminar-specific blood oxygenation level dependent we measured responses to a cascading oddball paradigm, to ground predictive auditory processes on the mesoscopic human cortical architecture. We show that the violation of predictions are potentially hierarchically organized and associated with responses in superficial layers of the planum polare and middle layers of the lateral temporal cortex. Moreover, we relate the updating of the brain's internal model to changes in deep layers. Using a modeling approach, we derive putative changes in neural dynamics while accounting for draining effects. Our results support the role of temporal cortical architecture in the implementation of predictive coding and highlight the ability of laminar fMRI to investigate mesoscopic processes in a large extent of temporal areas.

Differentiating BOLD and non-BOLD signals in fMRI time series using cross-cortical depth delay patterns

Over the past two decades, rapid advancements in magnetic resonance technology have significantly enhanced the imaging resolution of functional Magnetic Resonance Imaging (fMRI), far surpassing its initial capabilities. Beyond mapping brain functional architecture at unprecedented scales, high-spatial-resolution acquisitions have also inspired and enabled several novel analytical strategies that can potentially improve the sensitivity and neuronal specificity of fMRI. With small voxels, one can sample from different levels of the vascular hierarchy within the cerebral cortex and resolve the temporal progression of hemodynamic changes from parenchymal to pial vessels. We propose that this characteristic pattern of temporal progression across cortical depths can aid in distinguishing neurogenic blood-oxygenation-level-dependent (BOLD) signals from typical nuisance factors arising from non-BOLD origins, such as head motion and pulsatility. In this study, we examine the feasibility of applying cross-cortical depth temporal delay patterns to automatically categorize BOLD and non-BOLD signal components in modern-resolution BOLD-fMRI data. We construct an independent component analysis (ICA)-based framework for fMRI de-noising, analogous to previously proposed multi-echo (ME) ICA, except that here we explore the across-depth instead of across-echo dependence to distinguish BOLD and non-BOLD components. The efficacy of this framework is demonstrated using visual task data at three graded spatiotemporal resolutions (voxel sizes = 1.1, 1.5, and 2.0 mm isotropic at temporal intervals = 1700, 1120, and 928 ms). The proposed framework leverages prior knowledge of the spatiotemporal properties of BOLD-fMRI and serves as an alternative to ME-ICA for cleaning moderate- and high-spatial-resolution fMRI data when multi-echo acquisitions are not available.

Enabling brain-wide mapping of layer-specific functional connectivity at 3T via layer-dependent fMRI with draining-vein suppression

Layer-dependent functional magnetic resonance imaging (fMRI) is a promising yet challenging approach for investigating layer-specific functional connectivity (FC). Achieving a brain-wide mapping of layer-specific FC requires several technical advancements, including sub-millimeter spatial resolution, sufficient temporal resolution, functional sensitivity, global brain coverage, and high spatial specificity. Although gradient echo (GE)-based echo planar imaging (EPI) is commonly used for rapid fMRI acquisition, it faces significant challenges due to the draining-vein contamination. In this study, we addressed these limitations by integrating velocity-nulling (VN) gradients into a GE-BOLD fMRI sequence to suppress vascular signals from the vessels with fast-flowing velocity. The extravascular contamination from pial veins was mitigated using a GE-EPI sequence at 3T rather than 7T, combined with phase regression methods. Additionally, we incorporated advanced techniques, including simultaneous multislice (SMS) acceleration and NOise Reduction with DIstribution Corrected principal component analysis (NORDIC PCA) denoising, to improve temporal resolution, spatial coverage, and signal sensitivity. This resulted in a VN fMRI sequence with 0.9-mm isotropic spatial resolution, a repetition time (TR) of 4 seconds, and brain-wide coverage. The VN gradient strength was determined based on results from a button-pressing task. Using resting-state data, we validated layer-specific FC through seed-based analyses, identifying distinct connectivity patterns in the superficial and deep layers of the primary motor cortex (M1), with significant inter-layer differences. Further analyses with a seed in the primary sensory cortex (S1) demonstrated the reliability of the method. Brain-wide layer-dependent FC analyses yielded results consistent with prior literature, reinforcing the efficacy of VN fMRI in resolving layer-specific functional connectivity. Given the widespread availability of 3T scanners, this technical advancement has the potential for significant impact across multiple domains of neuroscience research.

Multilayer Network Analysis across Cortical Depths in Resting-State 7T fMRI

In graph theory, "multilayer networks" represent systems involving several interconnected topological levels. One example in neuroscience is the stratification of connections between different cortical depths or "laminae", which is becoming non-invasively accessible in humans using ultra-high-resolution functional MRI (fMRI). Here, we applied multilayer graph theory to examine functional connectivity across different cortical depths in humans, using 7T fMRI (1-mm3 voxels; 30 participants). Blood oxygenation level dependent (BOLD) signals were derived from five depths between the white matter and pial surface. We compared networks where the inter-regional connections were limited to a single cortical depth only ("layer-by-layer matrices") to those considering all possible connections between areas and cortical depths ("multilayer matrix"). We utilized global and local graph theory features that quantitatively characterize network attributes including network composition, nodal centrality, path-based measures, and hub segregation. Detecting functional differences between cortical depths was improved using multilayer connectomics compared to the layer-by-layer versions. Superficial depths of the cortex dominated information transfer and deeper depths drove clustering. These differences were largest in frontotemporal and limbic regions. fMRI functional connectivity across different cortical depths may contain neurophysiologically relevant information; thus, multilayer connectomics could provide a methodological framework for studies on how information flows across this stratification.

Closed-loop fMRI at the mesoscopic scale of columns and layers: Can we do it and why would we want to?

Technological advances in fMRI including ultra-high magnetic fields (≥ 7 T) and acquisition methods that increase spatial specificity have paved the way for studies of the human cortex at the scale of layers and columns. This mesoscopic scale promises an improved mechanistic understanding of human cortical function so far only accessible to invasive animal neurophysiology. In recent years, an increasing number of studies have applied such methods to better understand the cortical function in perception and cognition. This future perspective article asks whether closed-loop fMRI studies could equally benefit from these methods to achieve layer and columnar specificity. We outline potential applications and discuss the conceptual and concrete challenges, including data acquisition and volitional control of mesoscopic brain activity. We anticipate an important role of fMRI with mesoscopic resolution for closed-loop fMRI and neurofeedback, yielding new insights into brain function and potentially clinical applications.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

Comparison of compartmental analytical Blood-Oxygen-Level-Dependent functional Magnetic Resonance Imaging models against Monte Carlo simulations performed over cortical micro-angiograms

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD fMRI) arises from a physiological and physical cascade of events taking place at the level of the cortical microvasculature which constitutes a medium with complex geometry. Several analytical models of the BOLD contrast have been developed, but these have not been compared directly against detailed bottom-up modeling methods. Using a 3D modeling method based on experimentally measured images of mice microvasculature and Monte Carlo simulations, we quantified the accuracy of two analytical models to predict the amplitude of the BOLD response from 1.5 to 7 T, for different echo time (TE) and for both gradient echo and spin echo acquisition protocols. We also showed that accounting for the tridimensional structure of the microvasculature results in more accurate prediction of the BOLD amplitude, even if the values for SO2 were averaged across individual vascular compartments. A secondary finding is that modeling the venous compartment as two individual compartments results in more accurate prediction of the BOLD amplitude compared with standard homogenous venous modeling, arising from the bimodal distribution of venous SO2 across the microvasculature in our data.

The temporal specificity of BOLD fMRI is systematically related to anatomical and vascular features of the human brain

The ability to detect fast responses with functional MRI depends on the speed of hemodynamic responses to neural activity, because hemodynamic responses act as a temporal low-pass filter which blurs rapid changes. However, the shape and timing of hemodynamic responses are highly variable across the brain and across stimuli. This heterogeneity of responses implies that the temporal specificity of fMRI signals, or the ability of fMRI to preserve fast information, could also vary substantially across the cortex. In this work we investigated how local differences in hemodynamic response timing affect the temporal specificity of fMRI. We used ultra-high field (7T) fMRI at high spatiotemporal resolution, studying the primary visual cortex (V1) as a model area for investigation. We used visual stimuli oscillating at slow and fast frequencies to probe the temporal specificity of individual voxels. As expected, we identified substantial variability in temporal specificity, with some voxels preserving their responses to fast neural activity more effectively than others. We investigated which voxels had the highest temporal specificity, and tested whether voxel timing was related to anatomical and vascular features. We found that low temporal specificity is only weakly explained by the presence of large veins or cerebral cortical depth. Notably, however, temporal specificity depended strongly on a voxel's position along the anterior-posterior anatomical axis of V1, with voxels within the calcarine sulcus being capable of preserving close to 25% of their amplitude as the frequency of stimulation increased from 0.05Hz to 0.20Hz, and voxels nearest to the occipital pole preserving less than 18%. These results indicate that detection biases in high-resolution fMRI will depend on the anatomical and vascular features of the area being imaged, and that these biases will differ depending on the timing of the underlying neuronal activity. While we attribute this variance primarily to hemodynamic effects, neuronal nonlinearities may also influence response timing. Importantly, this spatial heterogeneity of temporal specificity suggests that it could be exploited to achieve higher specificity in some locations, and that tailored data analysis strategies may help improve the detection and interpretation of fast fMRI responses.

Laminar dissociation of feedforward and feedback in high-level ventral visual cortex during imagery and perception

Visual imagery and perception share neural machinery but rely on different information flow. While perception is driven by the integration of sensory feedforward and internally generated feedback information, imagery relies on feedback only. This suggests that although imagery and perception may activate overlapping brain regions, they do so in informationally distinctive ways. Using lamina-resolved MRI at 7 T, we measured the neural activity during imagery and perception of faces and scenes in high-level ventral visual cortex at the mesoscale of laminar organization that distinguishes feedforward from feedback signals. We found distinctive laminar profiles for imagery and perception of scenes and faces in the parahippocampal place area and the fusiform face area, respectively. Our findings provide insight into the neural basis of the phenomenology of visual imagery versus perception and shed new light into the mesoscale organization of feedforward and feedback information flow in high-level ventral visual cortex.

Laminar multi-contrast fMRI at 7T allows differentiation of neuronal excitation and inhibition underlying positive and negative BOLD responses

A major challenge for human neuroimaging using functional MRI is the differentiation of neuronal excitation and inhibition which may induce positive and negative BOLD responses. Here we present an innovative multi-contrast laminar functional MRI technique that offers comprehensive and quantitative imaging of neurovascular (CBF, CBV, BOLD) and metabolic (CMRO2) responses across cortical layers at 7 Tesla. This technique was first validated through a finger-tapping experiment, revealing 'double-peak' laminar activation patterns within the primary motor cortex. By employing a ring-shaped visual stimulus that elicited positive and negative BOLD responses, we further observed distinct neurovascular and metabolic responses across cortical layers and eccentricities in the primary visual cortex. This suggests potential feedback inhibition of neuronal activities in both superficial and deep cortical layers underlying the negative BOLD signals in the fovea, and also illustrates the neuronal activities in visual areas adjacent to the activated eccentricities.

The contribution of the vascular architecture and cerebrovascular reactivity to the BOLD signal formation across cortical depth

Assessment of neuronal activity using blood oxygenation level-dependent (BOLD) is confounded by how the cerebrovascular architecture modulates hemodynamic responses. To understand brain function at the laminar level, it is crucial to distinguish neuronal signal contributions from those determined by the cortical vascular organization. Therefore, our aim was to investigate the purely vascular contribution in the BOLD signal by using vasoactive stimuli and compare that with neuronal-induced BOLD responses from a visual task. To do so, we estimated the hemodynamic response function (HRF) across cortical depth following brief visual stimulations under different conditions using ultrahigh-field (7 Tesla) functional (f)MRI. We acquired gradient-echo (GE)-echo-planar-imaging (EPI) BOLD, containing contributions from all vessel sizes, and spin-echo (SE)-EPI BOLD for which signal changes predominately originate from microvessels, to distinguish signal weighting from different vascular compartments. Non-neuronal hemodynamic changes were induced by hypercapnia and hyperoxia to estimate cerebrovascular reactivity and venous cerebral blood volume ( C B V v O 2 ). Results show that increases in GE HRF amplitude from deeper to superficial layers coincided with increased macrovascular C B V v O 2 . C B V v O 2 -normalized GE-HRF amplitudes yielded similar cortical depth profiles as SE, thereby possibly improving specificity to neuronal activation. For GE BOLD, faster onset time and shorter time-to-peak were observed toward the deeper layers. Hypercapnia reduced the amplitude of visual stimulus-induced signal responses as denoted by lower GE-HRF amplitudes and longer time-to-peak. In contrast, the SE-HRF amplitude was unaffected by hypercapnia, suggesting that these responses reflect predominantly neurovascular processes that are less contaminated by macrovascular signal contributions.

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.

Predictions and errors are distinctly represented across V1 layers

Popular accounts of mind and brain propose that the brain continuously forms predictions about future sensory inputs and combines predictions with inputs to determine what we perceive.1,2,3,4,5,6 Under "predictive processing" schemes, such integration is supported by the hierarchical organization of the cortex, whereby feedback connections communicate predictions from higher-level deep layers to agranular (superficial and deep) lower-level layers.7,8,9,10 Predictions are compared with input to compute the "prediction error," which is transmitted up the hierarchy from superficial layers of lower cortical regions to the middle layers of higher areas, to update higher-level predictions until errors are reconciled.11,12,13,14,15 In the primary visual cortex (V1), predictions have thereby been proposed to influence representations in deep layers while error signals may be computed in superficial layers. Despite the framework's popularity, there is little evidence for these functional distinctions because, to our knowledge, unexpected sensory events have not previously been presented in human laminar paradigms to contrast against expected events. To this end, this 7T fMRI study contrasted V1 responses to expected (75% likely) and unexpected (25%) Gabor orientations. Multivariate decoding analyses revealed an interaction between expectation and layer, such that expected events could be decoded with comparable accuracy across layers, while unexpected events could only be decoded in superficial laminae. Although these results are in line with these accounts that have been popular for decades, such distinctions have not previously been demonstrated in humans. We discuss how both prediction and error processes may operate together to shape our unitary perceptual experiences.

Evaluating the effect of denoising submillimeter auditory fMRI data with NORDIC

Functional magnetic resonance imaging (fMRI) has emerged as an essential tool for exploring human brain function. Submillimeter fMRI, in particular, has emerged as a tool to study mesoscopic computations. The inherently low signal-to-noise ratio (SNR) at submillimeter resolutions warrants the use of denoising approaches tailored at reducing thermal noise - the dominant contributing noise component in high resolution fMRI. NORDIC PCA is one of such approaches, and has been benchmarked against other approaches in several applications. Here, we investigate the effects that two versions of NORDIC denoising have on auditory submillimeter data. As investigating auditory functional responses poses unique challenges, we anticipated that the benefit of this technique would be especially pronounced. Our results show that NORDIC denoising improves the detection sensitivity and the reliability of estimates in submillimeter auditory fMRI data. These effects can be explained by the reduction of the noise-induced signal variability. However, we also observed a reduction in the average response amplitude (percent signal), which may suggest that a small amount of signal was also removed. We conclude that, while evaluating the effects of the signal reduction induced by NORDIC may be necessary for each application, using NORDIC in high resolution auditory fMRI studies may be advantageous because of the large reduction in variability of the estimated responses.

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.

Modeling venous bias in resting state functional MRI metrics

Resting-state (rs) functional magnetic resonance imaging (fMRI) is used to detect low-frequency fluctuations in the blood oxygen-level dependent (BOLD) signal across brain regions. Correlations between temporal BOLD signal fluctuations are commonly used to infer functional connectivity. However, because BOLD is based on the dilution of deoxyhemoglobin, it is sensitive to veins of all sizes, and its amplitude is biased by draining veins. These biases affect local BOLD signal location and amplitude, and may also influence BOLD-derived connectivity measures, but the magnitude of this venous bias and its relation to vein size and proximity is unknown. Here, veins were identified using high-resolution quantitative susceptibility maps and utilized in a biophysical model to investigate systematic venous biases on common local rsfMRI-derived measures. Specifically, we studied the impact of vein diameter and distance to veins on the amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), Hurst exponent (HE), regional homogeneity (ReHo), and eigenvector centrality values in the grey matter. Values were higher across all distances in smaller veins, and decreased with increasing vein diameter. Additionally, rsfMRI values associated with larger veins decrease with increasing distance from the veins. ALFF and ReHo were the most biased by veins, while HE and fALFF exhibited the smallest bias. Across all metrics, the amplitude of the bias was limited in voxel-wise data, confirming that venous structure is not the dominant source of contrast in these rsfMRI metrics. Finally, the models presented can be used to correct this venous bias in rsfMRI metrics.

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.

Cortical depth-dependent human fMRI of resting-state networks using EPIK

Introduction

Recent laminar-fMRI studies have substantially improved understanding of the evoked cortical responses in multiple sub-systems; in contrast, the laminar component of resting-state networks spread over the whole brain has been less studied due to technical limitations. Animal research strongly suggests that the supragranular layers of the cortex play a critical role in maintaining communication within the default mode network (DMN); however, whether this is true in this and other human cortical networks remains unclear.

Methods

Here, we used EPIK, which offers unprecedented coverage at sub-millimeter resolution, to investigate cortical broad resting-state dynamics with depth specificity in healthy volunteers.

Results

Our results suggest that human DMN connectivity is primarily supported by intermediate and superficial layers of the cortex, and furthermore, the preferred cortical depth used for communication can vary from one network to another. In addition, the laminar connectivity profile of some networks showed a tendency to change upon engagement in a motor task. In line with these connectivity changes, we observed that the amplitude of the low-frequency-fluctuations (ALFF), as well as the regional homogeneity (ReHo), exhibited a different laminar slope when subjects were either performing a task or were in a resting state (less variation among laminae, i.e., lower slope, during task performance compared to rest).

Discussion

The identification of varied laminar profiles concerning network connectivity, ALFF, and ReHo, observed across two brain states (task vs. rest) has major implications for the characterization of network-related diseases and suggests the potential diagnostic value of laminar fMRI in psychiatric disorders, e.g., to differentiate the cortical dynamics associated with disease stages linked, or not linked, to behavioral changes. The evaluation of laminar-fMRI across the brain encompasses computational challenges; nonetheless, it enables the investigation of a new dimension of the human neocortex, which may be key to understanding neurological disorders from a novel perspective.

Combining arterial blood contrast with BOLD increases fMRI intracortical contrast

BOLD fMRI is widely applied in human neuroscience but is limited in its spatial specificity due to a cortical-depth-dependent venous bias. This reduces its localization specificity with respect to neuronal responses, a disadvantage for neuroscientific research. Here, we modified a submillimeter BOLD protocol to selectively reduce venous and tissue signal and increase cerebral blood volume weighting through a pulsed saturation scheme (dubbed Arterial Blood Contrast) at 7 T. Adding Arterial Blood Contrast on top of the existing BOLD contrast modulated the intracortical contrast. Isolating the Arterial Blood Contrast showed a response free of pial-surface bias. The results suggest that Arterial Blood Contrast can modulate the typical fMRI spatial specificity, with important applications in in-vivo neuroscience.

Improved sensitivity and microvascular weighting of 3T laminar fMRI with GE-BOLD using NORDIC and phase regression

INTRODUCTION

Functional MRI with spatial resolution in the submillimeter domain enables measurements of activation across cortical layers in humans. This is valuable as different types of cortical computations, e.g., feedforward versus feedback related activity, take place in different cortical layers. Laminar fMRI studies have almost exclusively employed 7T scanners to overcome the reduced signal stability associated with small voxels. However, such systems are relatively rare and only a subset of those are clinically approved. In the present study, we examined if the feasibility of laminar fMRI at 3T could be improved by use of NORDIC denoising and phase regression.

METHODS

5 healthy subjects were scanned on a Siemens MAGNETOM Prisma 3T scanner. To assess across-session reliability, each subject was scanned in 3-8 sessions on 3-4 consecutive days. A 3D gradient echo EPI (GE-EPI) sequence was used for BOLD acquisitions (voxel size 0.82 mm isotopic, TR = 2.2 s) using a block design finger tapping paradigm. NORDIC denoising was applied to the magnitude and phase time series to overcome limitations in temporal signal-to-noise ratio (tSNR) and the denoised phase time series were subsequently used to correct for large vein contamination through phase regression.

RESULTS AND CONCLUSION

NORDIC denoising resulted in tSNR values comparable to or higher than commonly observed at 7T. Layer-dependent activation profiles could thus be extracted robustly, within and across sessions, from regions of interest located in the hand knob of the primary motor cortex (M1). Phase regression led to substantially reduced superficial bias in obtained layer profiles, although residual macrovascular contribution remained. We believe the present results support an improved feasibility of laminar fMRI at 3T.

The many layers of BOLD. The effect of hypercapnic and hyperoxic stimuli on macro- and micro-vascular compartments quantified by CVR, M, and CBV across cortical depth

Ultra-high field functional magnetic resonance imaging (fMRI) offers the spatial resolution to measure neuronal activity at the scale of cortical layers. However, cortical depth dependent vascularization differences, such as a higher prevalence of macro-vascular compartments near the pial surface, have a confounding effect on depth-resolved blood-oxygen-level dependent (BOLD) fMRI signals. In the current study, we use hypercapnic and hyperoxic breathing conditions to quantify the influence of all venous vascular and micro-vascular compartments on laminar BOLD fMRI, as measured with gradient-echo (GE) and spin-echo (SE) scan sequences, respectively. We find that all venous vascular and micro-vascular compartments are capable of comparable theoretical maximum signal intensities, as represented by the M-value parameter. However, the capacity for vessel dilation, as reflected by the cerebrovascular reactivity (CVR), is approximately two and a half times larger for all venous vascular compartments combined compared to the micro-vasculature at superficial layers. Finally, there is roughly a 35% difference in estimates of CBV changes between all venous vascular and micro-vascular compartments, although this relative difference was approximately uniform across cortical depth. Thus, our results suggest that fMRI BOLD signal differences across cortical depth are likely caused by differences in dilation properties between macro- and micro-vascular compartments.

Cerebral blood volume sensitive layer-fMRI in the human auditory cortex at 7T: Challenges and capabilities

The development of ultra high field fMRI signal readout strategies and contrasts has led to the possibility of imaging the human brain in vivo and non-invasively at increasingly higher spatial resolutions of cortical layers and columns. One emergent layer-fMRI acquisition method with increasing popularity is the cerebral blood volume sensitive sequence named vascular space occupancy (VASO). This approach has been shown to be mostly sensitive to locally-specific changes of laminar microvasculature, without unwanted biases of trans-laminar draining veins. Until now, however, VASO has not been applied in the technically challenging cortical area of the auditory cortex. Here, we describe the main challenges we encountered when developing a VASO protocol for auditory neuroscientific applications and the solutions we have adopted. With the resulting protocol, we present preliminary results of laminar responses to sounds and as a proof of concept for future investigations, we map the topographic representation of frequency preference (tonotopy) in the auditory cortex.

Cortical depth profiles of auditory and visual 7 T functional MRI responses in human superior temporal areas

Invasive neurophysiological studies in nonhuman primates have shown different laminar activation profiles to auditory vs. visual stimuli in auditory cortices and adjacent polymodal areas. Means to examine the underlying feedforward vs. feedback type influences noninvasively have been limited in humans. Here, using 1-mm isotropic resolution 3D echo-planar imaging at 7 T, we studied the intracortical depth profiles of functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) signals to brief auditory (noise bursts) and visual (checkerboard) stimuli. BOLD percent-signal-changes were estimated at 11 equally spaced intracortical depths, within regions-of-interest encompassing auditory (Heschl's gyrus, Heschl's sulcus, planum temporale, and posterior superior temporal gyrus) and polymodal (middle and posterior superior temporal sulcus) areas. Effects of differing BOLD signal strengths for auditory and visual stimuli were controlled via normalization and statistical modeling. The BOLD depth profile shapes, modeled with quadratic regression, were significantly different for auditory vs. visual stimuli in auditory cortices, but not in polymodal areas. The different depth profiles could reflect sensory-specific feedforward versus cross-sensory feedback influences, previously shown in laminar recordings in nonhuman primates. The results suggest that intracortical BOLD profiles can help distinguish between feedforward and feedback type influences in the human brain. Further experimental studies are still needed to clarify how underlying signal strength influences BOLD depth profiles under different stimulus conditions.

Towards functional spin-echo BOLD line-scanning in humans at 7T

OBJECTIVE

Neurons cluster into sub-millimeter spatial structures and neural activity occurs at millisecond resolutions; hence, ultimately, high spatial and high temporal resolutions are required for functional MRI. In this work, we implemented a spin-echo line-scanning (SELINE) sequence to use in high spatial and temporal resolution fMRI.

MATERIALS AND METHODS

A line is formed by simply rotating the spin-echo refocusing gradient to a plane perpendicular to the excited slice and by removing the phase-encoding gradient. This technique promises a combination of high spatial and temporal resolution (250 μm, 500 ms) and microvascular specificity of functional responses. We compared SELINE data to a corresponding gradient-echo version (GELINE).

RESULTS

We demonstrate that SELINE showed much-improved line selection (i.e. a sharper line profile) compared to GELINE, albeit at the cost of a significant drop in functional sensitivity.

DISCUSSION

This low functional sensitivity needs to be addressed before SELINE can be applied for neuroscientific purposes.

Improved Selectivity in 7 T Digit Mapping Using VASO-CBV

Functional magnetic resonance imaging (fMRI) at Ultra-high field (UHF, ≥ 7 T) benefits from significant gains in the BOLD contrast-to-noise ratio (CNR) and temporal signal-to-noise ratio (tSNR) compared to conventional field strengths (3 T). Although these improvements enabled researchers to study the human brain to unprecedented spatial resolution, the blood pooling effect reduces the spatial specificity of the widely-used gradient-echo BOLD acquisitions. In this context, vascular space occupancy (VASO-CBV) imaging may be advantageous since it is proposed to have a higher spatial specificity than BOLD. We hypothesized that the assumed higher specificity of VASO-CBV imaging would translate to reduced overlap in fine-scale digit representation maps compared to BOLD-based digit maps. We used sub-millimeter resolution VASO fMRI at 7 T to map VASO-CBV and BOLD responses simultaneously in the motor and somatosensory cortices during individual finger movement tasks. We assessed the cortical overlap in different ways, first by calculating similarity coefficient metrics (DICE and Jaccard) and second by calculating selectivity measures. In addition, we demonstrate a consistent topographical organization of the targeted digit representations (thumb-index-little finger) in the motor areas. We show that the VASO-CBV responses yielded less overlap between the digit clusters than BOLD, and other selectivity measures were higher for VASO-CBV too. In summary, these results were consistent across metrics and participants, confirming the higher spatial specificity of VASO-CBV compared to BOLD.

Improved laminar specificity and sensitivity by combining SE and GE BOLD signals

The most widely used gradient-echo (GE) blood oxygenation level-dependent (BOLD) contrast has high sensitivity, but low specificity due to draining vein contributions, while spin-echo (SE) BOLD approach at ultra-high magnetic fields is highly specific to neural active sites but has lower sensitivity. To obtain high specificity and sensitivity, we propose to utilize a vessel-size-sensitive filter to the GE-BOLD signal, which suppresses macrovascular contributions and to combine selectively retained microvascular GE-BOLD signals with the SE-BOLD signals. To investigate our proposed idea, fMRI with 0.8 mm isotropic resolution was performed on the primary motor and sensory cortices in humans at 7 T by implementing spin- and gradient-echo (SAGE) echo planar imaging (EPI) acquisition. Microvascular-passed sigmoidal filters were designed based upon the vessel-size-sensitive ΔR₂*/ΔR₂ value for retaining GE-BOLD signals originating from venous vessels with ≤ 45 μm and ≤ 65 μm diameter. Unlike GE-BOLD fMRI, the laminar profile of SAGE-BOLD fMRI with the vessel-size-sensitive filter peaked at ∼ 1.0 mm from the surface of the primary motor and sensory cortices, demonstrating an improvement of laminar specificity over GE-BOLD fMRI. Also, the functional sensitivity of SAGE BOLD at middle layers (0.75-1.5 mm) was improved by ∼ 80% to ∼100% when compared with SE BOLD. In summary, we showed that combined GE- and SE-BOLD fMRI with the vessel-size-sensitive filter indeed yielded improved laminar specificity and sensitivity and is therefore an excellent tool for high spatial resolution ultra-high filed (UHF)-fMRI studies for resolving mesoscopic functional units.

Magnetization transfer weighted laminar fMRI with multi-echo FLASH

Laminar functional magnetic resonance imaging (fMRI) using the gradient echo (GRE) blood oxygenation level dependent (BOLD) contrast is prone to signal changes arising from large unspecific venous vessels. Alternatives based on changes of cerebral blood volume (CBV) become more popular since it is expected that this hemodynamic response is dominant in microvasculature. One approach to sensitize the signal toward changes in CBV, and to simultaneously reduce unwanted extravascular (EV) BOLD blurring, is to selectively reduce gray matter (GM) signal via magnetization transfer (MT). In this work, we use off-resonant MT-pulses with a 3D FLASH readout to perform MT-prepared (MT-prep) laminar fMRI of the primary visual cortex (V1) at multiple echo times at 7 T. With a GRE-BOLD contrast without additional MT-weighting as reference, we investigated the influence of the MT-preparation on the shape and the echo time dependency of laminar profiles. Through numerical simulations, we optimized the sequence parameters to increase the sensitivity toward signal changes induced by changes in arterial CBV and to delineate the contributions of different compartments to the signal. We show that at 7 T, GM signals can be reduced by 30 %. Our laminar fMRI responses exhibit an increased signal change in the parenchyma at very short TE compared to a BOLD-only reference as a result of reduced EV signal intensity. By varying echo times, we could show that MT-prep results in less sensitivity toward unwanted signal changes based on changes in T₂*. We conclude that when accounting for nuclear overhauser enhancement effects in blood, off-resonant MT-prep combined with efficient short TE readouts can become a promising method to reduce unwanted EV venous contributions in GRE-BOLD and/or to allow scanning at much shorter echo times without incurring a sensitivity penalty in laminar fMRI.

Mesoscopic in vivo human T2* dataset acquired using quantitative MRI at 7 Tesla

Mesoscopic (0.1-0.5 mm) interrogation of the living human brain is critical for advancing neuroscience and bridging the resolution gap with animal models. Despite the variety of MRI contrasts measured in recent years at the mesoscopic scale, in vivo quantitative imaging of T₂^* has not been performed. Here we provide a dataset containing empirical T₂^* measurements acquired at 0.35 × 0.35 × 0.35 mm³ voxel resolution using 7 Tesla MRI. To demonstrate unique features and high quality of this dataset, we generate flat map visualizations that reveal fine-scale cortical substructures such as layers and vessels, and we report quantitative depth-dependent T₂^* (as well as R₂^*) values in primary visual cortex and auditory cortex that are highly consistent across subjects. This dataset is freely available at https://doi.org/10.17605/OSF.IO/N5BJ7, and may prove useful for anatomical investigations of the human brain, as well as for improving our understanding of the basis of the T₂^*-weighted (f)MRI signal.

What has vision science taught us about functional MRI?

In the domain of human neuroimaging, much attention has been paid to the question of whether and how the development of functional magnetic resonance imaging (fMRI) has advanced our scientific knowledge of the human brain. However, the opposite question is also important; how has our knowledge of the brain advanced our understanding of fMRI? Here, we discuss how and why scientific knowledge about the human and animal visual system has been used to answer fundamental questions about fMRI as a brain measurement tool and how these answers have contributed to scientific discoveries beyond vision science.

Laminar-specific functional connectivity mapping with multi-slice line-scanning fMRI

Despite extensive studies detecting laminar functional magnetic resonance imaging (fMRI) signals to illustrate the canonical microcircuit, the spatiotemporal characteristics of laminar-specific information flow across cortical regions remain to be fully investigated in both evoked and resting conditions at different brain states. Here, we developed a multislice line-scanning fMRI (MS-LS) method to detect laminar fMRI signals in adjacent cortical regions with high spatial (50 μm) and temporal resolution (100 ms) in anesthetized rats. Across different trials, we detected either laminar-specific positive or negative blood-oxygen-level-dependent (BOLD) responses in the surrounding cortical region adjacent to the most activated cortex under the evoked condition. Specifically, in contrast to typical Layer (L) 4 correlation across different regions due to the thalamocortical projections for trials with positive BOLD, a strong correlation pattern specific in L2/3 was detected for trials with negative BOLD in adjacent regions, which indicated brain state-dependent laminar-fMRI responses based on corticocortical interaction. Also, in resting-state (rs-) fMRI study, robust lag time differences in L2/3, 4, and 5 across multiple cortices represented the low-frequency rs-fMRI signal propagation from caudal to rostral slices. In summary, our study provided a unique laminar fMRI mapping scheme to better characterize trial-specific intra- and inter-laminar functional connectivity in evoked and resting-state MS-LS.

Laminar functional magnetic resonance imaging in vision research

Magnetic resonance imaging (MRI) scanners at ultra-high magnetic fields have become available to use in humans, thus enabling researchers to investigate the human brain in detail. By increasing the spatial resolution, ultra-high field MR allows both structural and functional characterization of cortical layers. Techniques that can differentiate cortical layers, such as histological studies and electrode-based measurements have made critical contributions to the understanding of brain function, but these techniques are invasive and thus mainly available in animal models. There are likely to be differences in the organization of circuits between humans and even our closest evolutionary neighbors. Thus research on the human brain is essential. Ultra-high field MRI can observe differences between cortical layers, but is non-invasive and can be used in humans. Extensive previous literature has shown that neuronal connections between brain areas that transmit feedback and feedforward information terminate in different layers of the cortex. Layer-specific functional MRI (fMRI) allows the identification of layer-specific hemodynamic responses, distinguishing feedback and feedforward pathways. This capability has been particularly important for understanding visual processing, as it has allowed researchers to test hypotheses concerning feedback and feedforward information in visual cortical areas. In this review, we provide a general overview of successful ultra-high field MRI applications in vision research as examples of future research.

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.

Pre-processing of Sub-millimeter GE-BOLD fMRI Data for Laminar Applications

Over the past 30 years, brain function has primarily been evaluated non-invasively using functional magnetic resonance imaging (fMRI) with gradient-echo (GE) sequences to measure blood-oxygen-level-dependent (BOLD) signals. Despite the multiple advantages of GE sequences, e.g., higher signal-to-noise ratio, faster acquisitions, etc., their relatively inferior spatial localization compromises the routine use of GE-BOLD in laminar applications. Here, in an attempt to rescue the benefits of GE sequences, we evaluated the effect of existing pre-processing methods on the spatial localization of signals obtained with EPIK, a GE sequence that affords voxel volumes of 0.25 mm3 with near whole-brain coverage. The methods assessed here apply to both task and resting-state fMRI data assuming the availability of reconstructed magnitude and phase images.

Imaging of the pial arterial vasculature of the human brain in vivo using high-resolution 7T time-of-flight angiography

The pial arterial vasculature of the human brain is the only blood supply to the neocortex, but quantitative data on the morphology and topology of these mesoscopic arteries (diameter 50–300 µm) remains scarce. Because it is commonly assumed that blood flow velocities in these vessels are prohibitively slow, non-invasive time-of-flight magnetic resonance angiography (TOF-MRA)—which is well suited to high 3D imaging resolutions—has not been applied to imaging the pial arteries. Here, we provide a theoretical framework that outlines how TOF-MRA can visualize small pial arteries in vivo, by employing extremely small voxels at the size of individual vessels. We then provide evidence for this theory by imaging the pial arteries at 140 µm isotropic resolution using a 7 Tesla (T) magnetic resonance imaging (MRI) scanner and prospective motion correction, and show that pial arteries one voxel width in diameter can be detected. We conclude that imaging pial arteries is not limited by slow blood flow, but instead by achievable image resolution. This study represents the first targeted, comprehensive account of imaging pial arteries in vivo in the human brain. This ultra-high-resolution angiography will enable the characterization of pial vascular anatomy across the brain to investigate patterns of blood supply and relationships between vascular and functional architecture.

Resting state fMRI connectivity is sensitive to laminar connectional architecture in the human brain

Previous invasive studies indicate that human neocortical graymatter contains cytoarchitectonically distinct layers, with notable differences in their structural connectivity with the rest of the brain. Given recent improvements in the spatial resolution of anatomical and functional magnetic resonance imaging (fMRI), we hypothesize that resting state functional connectivity (FC) derived from fMRI is sensitive to layer-specific thalamo-cortical and cortico-cortical microcircuits. Using sub-millimeter resting state fMRI data obtained at 7 T, we found that: (1) FC between the entire thalamus and cortical layers I and VI was significantly stronger than between the thalamus and other layers. Furthermore, FC between somatosensory thalamus (ventral posterolateral nucleus, VPL) and layers IV, VI of the primary somatosensory cortex were stronger than with other layers; (2) Inter-hemispheric cortico-cortical FC between homologous regions in superficial layers (layers I-III) was stronger compared to deep layers (layers V-VI). These findings are in agreement with structural connections inferred from previous invasive studies that showed that: (i) M-type neurons in the entire thalamus project to layer-I; (ii) Pyramidal neurons in layer-VI target all thalamic nuclei, (iii) C-type neurons in the VPL project to layer-IV and receive inputs from layer-VI of the primary somatosensory cortex, and (iv) 80% of collosal projecting neurons between homologous cortical regions connect superficial layers. Our results demonstrate for the first time that resting state fMRI is sensitive to structural connections between cortical layers (previously inferred through invasive studies), specifically in thalamo-cortical and cortico-cortical networks.

Cortical depth-dependent modeling of visual hemodynamic responses

A physiologically based three-dimensional (3D) hemodynamic model is developed to predict the experimentally observed blood oxygen level dependent (BOLD) responses versus the cortical depth induced by visual stimuli. Prior 2D approximations are relaxed in order to analyze 3D blood flow dynamics as a function of cortical depth. Comparison of the predictions with experimental data for evoked stimuli demonstrates that the full 3D model performs at least as well as previous approaches while remaining parsimonious. In particular, the 3D model requires significantly fewer assumptions and model parameters than previous models such that there is no longer need to define depth-specific parameter values for spatial spreading, peak amplitude, and hemodynamic velocity.

Simultaneous pure T2 and varying T2'-weighted BOLD fMRI using Echo Planar Time-resolved Imaging for mapping cortical-depth dependent responses

Spin-echo (SE) BOLD fMRI has high microvascular specificity, and thus provides a more reliable means to localize neural activity compared to conventional gradient-echo BOLD fMRI. However, the most common SE BOLD acquisition method, SE-EPI, is known to suffer from T2' contrast contamination with undesirable draining vein bias. To address this, in this study, we extended a recently developed distortion/blurring-free multi-shot EPI technique, Echo-Planar Time-resolved Imaging (EPTI), to cortical-depth dependent SE-fMRI at 7T to test whether it could provide purer SE BOLD contrast with minimal T2' contamination for improved neuronal specificity. From the same acquisition, the time-resolved feature of EPTI also provides a series of asymmetric SE (ASE) images with varying T2' weightings, and enables extraction of data equivalent to conventional SE EPI with different echo train lengths (ETLs). This allows us to systematically examine how T2'-contribution affects different SE acquisition strategies using a single dataset. A low-rank spatiotemporal subspace reconstruction was implemented for the SE-EPTI acquisition, which incorporates corrections for both shot-to-shot phase variations and dynamic B0 drifts. SE-EPTI was used in a visual task fMRI experiment to demonstrate that i) the pure SE image provided by EPTI results in the highest microvascular specificity; ii) the ASE EPTI series, with a graded introduction of T2' weightings at time points farther away from the pure SE, show a gradual sensitivity increase along with increasing draining vein bias; iii) the longer ETL seen in conventional SE EPI acquisitions will induce more draining vein bias. Consistent results were observed across multiple subjects, demonstrating the robustness of the proposed technique for SE-BOLD fMRI with high specificity.

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.

Layer-dependent functional connectivity methods

Recent methodological advances in fMRI contrast and readout strategies have allowed researchers to approach the mesoscopic spatial regime of cortical layers. This has revolutionized the ability to map cortical information processing within and across brain systems. However, until recently, most layer-fMRI studies have been confined to primary cortices using basic block-design tasks and macro-vascular-contaminated sequence contrasts. To become an established method for user-friendly applicability in neuroscience practice, layer-fMRI acquisition and analysis methods need to be extended to more flexible connectivity-based experiment designs; they must be able to capture subtle changes in brain networks of higher-order cognitive areas, and they should not be spatially biased with unwanted vein signals. In this article, we review the most pressing challenges of layer-dependent fMRI for large-scale neuroscientific applicability and describe recently developed acquisition methodologies that can resolve them. In doing so, we review technical tradeoffs and capabilities of modern MR-sequence approaches to achieve measurements that are free of locally unspecific vein signal, with whole-brain coverage, sub-second sampling, high resolutions, and with a combination of those capabilities. The presented approaches provide whole-brain layer-dependent connectivity data that open a new window to investigate brain network connections. We exemplify this by reviewing a number of candidate tools for connectivity analyses that will allow future studies to address new questions in network neuroscience. The considered network analysis tools include: hierarchy mapping, directional connectomics, source-specific connectivity mapping, and network sub-compartmentalization. We conclude: Whole-brain layer-fMRI without large-vessel contamination is applicable for human neuroscience and opens the door to investigate biological mechanisms behind any number of psychological and psychiatric phenomena, such as selective attention, hallucinations and delusions, and even conscious perception.

The Global Configuration of Visual Stimuli Alters Co-Fluctuations of Cross-Hemispheric Human Brain Activity

We tested how a stimulus gestalt, defined by the neuronal interaction between local and global features of a stimulus, is represented within human primary visual cortex (V1). We used high-resolution fMRI, which serves as a surrogate of neuronal activation, to measure co-fluctuations within subregions of V1 as (male and female) subjects were presented with peripheral stimuli, each with different global configurations. We found stronger cross-hemisphere correlations when fine-scale V1 cortical subregions represented parts of the same object compared with different objects. This result was consistent with the vertical bias in global processing and, critically, was independent of the task and local discontinuities within objects. Thus, despite the relatively small receptive fields of neurons within V1, global stimulus configuration affects neuronal processing via correlated fluctuations between regions that represent different sectors of the visual field.SIGNIFICANCE STATEMENT We provide the first evidence for the impact of global stimulus configuration on cross-hemispheric fMRI fluctuations, measured in human primary visual cortex. Our results are consistent with changes in the level of γ-band synchrony, which has been shown to be affected by global stimulus configuration, being reflected in the level fMRI co-fluctuations. These data help narrow the gap between knowledge of global stimulus configuration encoding at the single-neuron level versus at the behavioral level.

Point-spread function of the BOLD response across columns and cortical depth in human extra-striate cortex

Columns and layers are fundamental organizational units of the brain. Well known examples of cortical columns are the ocular dominance columns (ODCs) in primary visual cortex and the column-like stripe-based arrangement in the second visual area V2. The spatial scale of columns and layers is beyond the reach of conventional neuroimaging, but the advent of high field magnetic resonance imaging (MRI) scanners (UHF, 7 Tesla and above) has opened the possibility to acquire data at this spatial scale, in-vivo and non-invasively in humans. The most prominent non-invasive technique to measure brain function is blood oxygen level dependent (BOLD) fMRI, measuring brain activity indirectly, via changes in hemodynamics. A key determinant of the ability of high-resolution BOLD fMRI to accurately resolve columns and layers is the point-spread function (PSF) of the BOLD response in relation to the spatial extent of neuronal activity. In this study we take advantage of the stripe-based arrangement present in visual area V2, coupled with sub-millimetre anatomical and gradient-echo BOLD (GE BOLD) acquisition at 7 T to obtain PSF estimates and along cortical depth in human participants. Results show that the BOLD PSF is maximal in the superficial part of the cortex (1.78 mm), and it decreases with increasing cortical depth (0.83 mm close to white matter).

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

Recent studies have demonstrated that fast fMRI can track neural activity well above the temporal limit predicted by the canonical hemodynamic response model. While these findings are promising, the biophysical mechanisms underlying these fast fMRI phenomena remain underexplored. In this study, we discuss two aspects of the hemodynamic response, complementary to several existing hypotheses, that can accommodate faster fMRI dynamics beyond those predicted by the canonical model. First, we demonstrate, using both visual and somatosensory paradigms, that the timing and shape of hemodynamic response functions (HRFs) vary across graded levels of stimulus intensity-with lower-intensity stimulation eliciting faster and narrower HRFs. Second, we show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF. Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. It is further noteworthy that, while the present investigations focus on fast neural responses, our findings suggest that a revised hemodynamic model may benefit the many fMRI studies using paradigms with wide ranges of contrast levels (e.g., resting or naturalistic conditions) or with modern, high-resolution MR acquisitions.

Correcting for Superficial Bias in 7T Gradient Echo fMRI

The arrival of submillimeter ultra high-field fMRI makes it possible to compare activation profiles across cortical layers. However, the blood oxygenation level dependent (BOLD) signal measured by gradient echo (GE) fMRI is biased toward superficial layers of the cortex, which is a serious confound for laminar analysis. Several univariate and multivariate analysis methods have been proposed to correct this bias. We compare these methods using computational simulations of 7T fMRI data from regions of interest (ROI) during a visual attention paradigm. We also tested the methods on a pilot dataset of human 7T fMRI data. The simulations show that two methods–the ratio of ROI means across conditions and a novel application of Deming regression–offer the most robust correction for superficial bias. Deming regression has the additional advantage that it does not require that the conditions differ in their mean activation over voxels within an ROI. When applied to the pilot dataset, we observed strikingly different layer profiles when different attention metrics were used, but were unable to discern any differences in laminar attention across layers when Deming regression or ROI ratio was applied. Our simulations demonstrates that accurate correction of superficial bias is crucial to avoid drawing erroneous conclusions from laminar analyses of GE fMRI data, and this is affirmed by the results from our pilot 7T fMRI data.