Laminar fMRI refers to the study of functional activation within the cerebral cortex, with the goal of detecting distinct functional activity within cortical layers, and is an emerging application of high-resolution fMRI. Although individual cortical layers cannot be resolved with current human fMRI techniques, and hemodynamic coupling and variation of fMRI signals across layers is incompletely understood, because of the roles cortical layers play in distributed neuronal processing measuring layer-specific activation is key to understanding brain circuitry, which motivates work towards surmounting these difficulties. This presentation will introduce laminar fMRI, summarize recent advances, and focus on challenges faced when interpreting these data.
Continued advances in MRI technologies such as ultra-high-field systems and massively-parallel receive coil arrays provide the necessary sensitivity and encoding capabilities to produce fMRI acquisitions with higher spatiotemporal resolution. With current scanners it is increasingly possible to achieve sub-millimeter voxel sizes with sub-second sampling rates, sufficient SNR, and broad coverage to investigate large regions of the brain. As the spatial and temporal resolution of fMRI increase, the natural question arises: Is the ability of today’s fMRI to accurately map fine-scale functional organization and neuronal activity limited by the instrumentation, i.e. by the spatial and temporal sampling we can achieve with modern MRI systems, or by the spatial and temporal specificity of the hemodynamic signals utilized in fMRI? That is, is the limiting factor related to the “biological resolution” of these signals, which is determined by the brain’s ability to locally regulate changes in blood flow, or by the instrumental resolution achievable by our imaging systems? Recent optical imaging studies have demonstrated that changes in blood flow regulation occurring in response to nearby neuronal activity are far more precise than previously believed, suggesting that responses may be localized within individual cortical columns or layers; nevertheless, currently the ultimate resolution achievable by fMRI is still unknown.
Laminar fMRI therefore provides a test-bed to examine the ultimate limits of neuronal specificity of fMRI signals. Just as previous studies of cortical columns helped to demonstrate fine-scale blood flow regulation at the millimeter scale in the direction tangential to the cortex, current studies of cortical layers can help to establish how fine-scale blood flow regulation is at the sub-millimeter scale in the direction radial to the cortex. This is one key motivation driving the many recent and ongoing laminar fMRI studies.
Another motivation is the role of cortical layers in brain circuitry. It is well-known that, while neurons within cortical columns often possess similar functional properties, there are often distinct functional properties across the cortical layers, even within a column. Perhaps the best-known example is the common pattern of input and output projections along feedforward, feedback, and lateral pathways [1] connecting cortical areas to other cortical and subcortical regions. While these patterns of connections are in reality quite complex [2] and can vary systematically across the brain [3,4], mapping out these multi-area circuits will be essential for understanding human brain function. This long-term goal has inspired many groups to investigate whether it may be possible to localize functional activation within cortical layers noninvasively with fMRI.
This educational lecture will focus on the application of high-resolution fMRI to the study of cortical layers, mainly in humans. The material of this lecture is taken in part from recent review articles written by the presenter [5,6], as well as from a series of review articles currently in press [7–17] from a Special Issue of NeuroImage on this topic entitled “Prospects for cortical laminar MRI: functional and anatomical imaging of cerebral cortical layers”.
The ultimate spatial specificity of any hemodynamic-based measure of neuronal activity is the scale at which blood flow can be regulated in brain tissue, and therefore is determined by the spacing of the structures (vascular sphincters and smooth muscle) that actively control vessel diameter and the consequent vascular resistance and blood flow. Because smooth muscle is present only at the arteriole level, it was long believed that the ultimate specificity was dictated by the spacing of diving arterioles in cerebral cortex—on the order of 1 mm in humans [18,19]. However, in some brain regions blood flow apparently can be diverted to specific cortical layers [20–22] and regulated precisely within a continuous capillary network in specialized regions of the brain [23], a fine control made possible by several potential mechanisms [24–29]. This suggests that blood flow may be regulated at a spatial scale far smaller than our current voxel dimensions, indicating that fMRI will benefit from acquisition development efforts that provide smaller voxels with higher sensitivity measurements of the hemodynamic response [30].
Overall, there is evidence for fine-scale blood flow regulation that could support laminar fMRI. However, because many findings are based on invasive experiments using model animals under anesthesia and/or sedation, which impacts neuronal activity, hemodynamics, and neurovascular coupling [31], because of the differences in microvascular anatomy between humans and small animals [14], and may vary across brain regions [7], the ability of fMRI to detect functional differences across cortical layers ultimately must be established empirically through carefully designed and executed human fMRI experiments.
Given the limited biological resolution of fMRI, if similar functional properties were detected across depths at a particular location of cortex, this similarity could reflect either true functional similarity at the neuronal level or the limited spatial specificity of the BOLD response, i.e., the signals measured within small voxels sampling across cortical depths could be intrinsically coupled through the local vasculature—even if blood flow can be diverted to specific layers. Unfortunately, there is a grid-like regularity of the local vascular anatomy that closely resembles the layout of cortical columns and layers. It is well known that the principal arterioles and venules, which supply and drain the parenchymal capillary bed, are small vessels that are oriented perpendicularly to the cortical surface. These small intracortical vessels reflect a potential coupling of the hemodynamic signals across depths, both on the arterial size and the venous side, with implications for many forms of fMRI contrast. Due to their small sizes, it is possible that even techniques with microvascular weighting could still be sensitive to BOLD signal changes within these radial vessels [32]. These radial vessels would impose a spatial spreading perpendicular to the cortical surface (which implies a spatial asymmetry in the biological point-spread function [33]). For this reason, to establish that similar BOLD responses across depths reflect a true similarity of function at the neuronal level would require accounting for the potential artifactual coupling imparted by the spatial spread of the BOLD response.
This vascular coupling of BOLD signals across depths impacts the ability to detect functional activation within individual cortical layers as well. Because small intracortical venules drain deoxygenated blood from deeper layers up through superficial layers up to the pial vasculature, neuronal activation within the deep layers can trigger BOLD changes across all layers due to this downstream effect. To account for this effect, recent studies have attempted to build explicit models of intracortical fMRI signal changes that accounts for the vascular coupling across layers induced by the radial intracortical vasculature [34].
For the intracortical fMRI analysis of columns and layers described above, it is necessary to know the basic geometry of these architectonic features and how they shift and bend with the folding pattern. It is well-known that the position of the cortical layers varies systematically with the cortical folding pattern—perhaps counterintuitively, the infragranular (lowermost) layers are compressed in sulci and expanded in gyri, and the supragranular (uppermost) layers are compressed in gyri and expanded in sulci. This compression and expansion causes an exaggeration of the curvature of Layer IV within the cortex and causes the depth of Layer IV to change with the curvature of the cortical ribbon [35–37].
A commonly used approach to defining cortical layer positions is to exploit the observation by Bok that the position of the granular layer (approximately Layer IV) provides an equal volume of cortex in the supragranular and infragranular layers within a cone-shaped region centered at each point of the cortex [35]. This equi-volume principle has been implemented computationally and compared to both the solution to the Laplace equation and to sampling simply by cortical depth (a.k.a. equi-distant sampling) using high-resolution ex vivo brain data (in which the layers could be clearly detected anatomically); the equi-volume sampling was found to provide a better prediction of the layer positions [38].
The earliest laminar fMRI studies were conducted in small animal models [42–46] and demonstrated a heterogenous fMRI response across depths in which a strong activation was detected in the central layers corresponding roughly to the position of Layer IV (which is often an input layer along feed-forward pathways). Subsequent work demonstrated that intracortical signal peaks could be robustly detected at specific cortical depths through exploiting either temporal differences in signal onset or maximal response [16,42,47] or various forms of fMRI contrast including high-resolution BOLD or non-BOLD imaging [44–46,48,49].
Subsequent high-resolution fMRI studies in both non-human primates [50–52] and in humans [33,53–56] demonstrated consistent differences in the fMRI signal across depths, which can be attributable to differences in neuronal responses combined with differences in the distribution of microvasculature and differences in the proximity to microvasculature across cortical depths. More recent work has demonstrated either changes in the laminar profiles or shifting peaks in the intracortical signal by contrasting responses to specific stimuli or tasks with suitable controls [41,57–59].
Because of the potential biases of the intracortical fMRI signal imposed by the heterogeneous distribution of both micro- and microvasculature across depths, and the local coupling of the fMRI signals imparted by radial vessels, the laminar activation profile generated from any one stimulus or task may reflect both neuronal activation differences and vascular differences across layers, and therefore interpretation can be challenging. Further complicating the interpretation of the shape of the profile is the fact that the expected profile for a simple stimulus that activates all layers is different for different pulse sequences, and depends on the sensitivity of the sequence to different vessel sizes [56].
Therefore, it is common practice to compare laminar profiles across multiple stimuli to identify differences (and commonalities) across profiles to attempt to infer how the underlying neuronal activity differs between conditions. This complication in interpreting the profiles underscores the importance of well-designed control experiments when performing laminar fMRI.
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