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Exploration of human visual cortex using high spatial resolution functional magnetic resonance imaging
Author’s Accepted Manuscript Exploration of Human Visual Cortex Using High Spatial Resolution Functional Magnetic Resonance Imaging Kang cheng www.elsevier.com
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S1053-8119(16)30638-3 http://dx.doi.org/10.1016/j.neuroimage.2016.11.018 YNIMG13566
To appear in: NeuroImage Received date: 29 August 2016 Accepted date: 8 November 2016 Cite this article as: Kang cheng, Exploration of Human Visual Cortex Using High Spatial Resolution Functional Magnetic Resonance Imaging, NeuroImage, http://dx.doi.org/10.1016/j.neuroimage.2016.11.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Exploration of Human Visual Cortex Using High Spatial Resolution Functional Magnetic Resonance Imaging Kang Cheng Support Unit for Functional Magnetic Resonance Imaging, and Laboratory for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: +81 48 467 9730; E-mail: [email protected] Abstract In this review focusing primarily on the work conducted in my group at RIKEN Brain Science Institute, I will first briefly summarize what we have achieved in mapping columnar organizations in human primary visual cortex using blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI), including ocular dominance columns, temporal frequency dependent domains, and orientation selective columns. I will then touch upon a couple of recent successful attempts in the field in mapping functional architectures in human extrastriate cortices, including human middle temporal complex and secondary and tertiary visual areas (V2 and V3), and discuss what we have learned regarding the spatial specificity of BOLD fMRI. Finally, I will offer some of my personal thoughts on how functional architectures may be organized in relation to underlying microvasculature and how such functional architectures may be experimentally explored.
Keywords: High spatial resolution fMRI, human, striate cortex (V1), extrastraite cortex, pointe spread function, microvasculature 1
Imaging ocular dominance columns in human primary visual cortex Ever since the inception of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI), its spatial specificity has always been the concern as well as the hope. Some of the earliest efforts have been made in order to use BOLD fMRI to map functional architectures in human visual cortex, in particular, columnar structures. Indeed, several sites that owned 4 Tesla whole-body MRI systems in the middle of the 1990s, including the Center for Magnetic Resonance Research, University of Minnesota in the United States, the Robarts Research Institute, the University of Western Ontario in Canada, and RIKEN Brain Science Institute in Japan, were all involved in mapping human ocular dominance columns (ODCs). The combined efforts in these studies, some of which are briefly reviewed below, have greatly advanced our understanding of the spatial specificity of BOLD fMRI, and have helped in establishing certain techniques and practical experimental procedures for future exploration of columnar structures in human visual cortex and beyond. The system of ODCs is one of the clearest examples of the distinct functional architecture within the cortex. Neurons in the primary visual cortex (striate cortex, V1) receive input primarily from either the left eye or right eye and are grouped in periodic left-right eye stripes on the cortical surface (or slabs if viewed through the cortical thickness) that run roughly parallel to the equi-eccentricity representations. Although neurons are less purely monocular in the upper and lower layers, the dominance by one eye or the opposite eye is consistent throughout six cortical layers. Originally discovered in cats and monkeys using the single-neuron recording technique, the spatial layout of 2
ODCs was subsequently revealed using several anatomical approaches, including autoradiographic demonstration of ODCs in monkey V1 by means of transneuronal transport after the tracer was injected into one eye (Hubel and Wiesel, 1972; Wiesel et al., 1974), and revelation of the ODC pattern with cytochrome oxidase (CO) stain, which selectively showed reduced oxidative activity in ODCs corresponding to the eye that was experimentally removed (Horton, 1984), and later mapped out with intrinsic optical imaging methods (Blasdel and Salama, 1986; Ts’o et al., 1990). Importantly, ODCs, to the best of our knowledge so far, are the only hardwired system in visual cortex, as monocular geniculocortical afferents serving the two eyes are strictly segregated in V1. ODCs in humans resemble those in monkeys. In a few rare cases, the results from human post-mortem brains stained with CO demonstrate that ODCs in humans, with a similar layout as that in monkeys, measure approximately 1 mm in width, which is roughly twice as wide as the width of ODCs in monkeys (Horton and Hedley-Whyne, 1984; Horton et al., 1990; Adams et al., 2007). Human ODCs, thus, provide a perfect model for testing the spatial resolvability of BOLD fMRI. Ravi Menon, when he was still at the University of Minnesota, first showed that ocular dominance, that is, the differential response to the photic stimulation of either the left eye or the right eye, could be revealed in humans using BOLD fMRI (Menon et al., 1997). Later, after moving to the University of Western Ontario, Ravi and his student Bradley Goodyear, further showed that ODC patterns in individual subjects could be mapped out, suggesting that BOLD fMRI may possess a point spread function (PSF) that is fine enough to resolve functional structures at columnar resolution (Menon and 3
Goodyear, 1999). At about the same time, Peter Dechent and Jens Frahm also published a paper, using a 2 Tesla scanner and a FLASH pulse sequence, showing that ODCs in humans can be directly mapped out (Dechent and Frahm, 2000). We commenced our ODC project relatively late, in hope, primarily, to develop high spatial resolution fMRI techniques through this project for imaging columnar structures in extrastriate cortices. The technologies at that time, compared with what are available today, were rather primitive, as most of our experiments were conducted using a single-loop or a two-loop quadrature RF coil with a single reception channel. Perhaps because we were extremely concerned with the T2* blurring of images acquired using BOLD fMRI (one of my close collaborators, our MR physicist, R. Allen Waggoner, was trained by Dr. Eiichi Fukushima, famous for his “MR Nuts and Bolts” book (Fukushima and Roeder, 1981), in which the issue of T2* blurring was clearly described), in order to shorten the echo train length and achieve a relatively high signal to noise ratio (SNR), we used a multi-shot echo-planar imaging (EPI) pulse sequence, which made the volume TR very long even for acquiring a small number of slices (9.6 s with three interleaved slices). This was a problem, as this would make it extremely difficult to capture the early positive BOLD responses following visual stimulation, which are spatially more localized (Goodyear and Menon, 2001). At that time, influenced largely by works conducted using intrinsic optical imaging, which, similar to BOLD fMRI, relies on hemodynamic responses as mapping signals, it was commonly believed that the spatial specificity might be lost if later positive responses are used as a mapping signal. This is true if responses from a single condition (e.g., left-eye stimulation versus baseline) are used as a mapping 4
signal. However, we had noticed then, from several published intrinsic optical imaging papers (e.g., Malonek and Grinvald, 1996; Grinvald et al., 2000), as well as observations from our own optical imaging experiments, that the differential mapping signal (i.e., the subtraction between images acquired when the left eye is stimulated and those acquired when the right eye is stimulated) actually preserves a consistent spatially resolved pattern for an extended period, even after the stimulation is switched off (this is now well established; for explanation and demonstration of this statement, see Figure 2 and legends in an excellent recent review by Fukuda and colleagues, 2016). Faced with the dilemma of likely reduced differential responses to the stimulation of the two eyes when the stimulation is prolonged, but potentially increased image SNR through averaging of larger number of images, we decided to run our experiments using a prolonged stimulation paradigm (this was, in a sense, a desperate choice; each eye each time was stimulated for up to two minutes). Even to this day, I still owe my heartfelt thanks to several of my dedicated subjects for their efforts in participating in repeated experiments, in which each single session would last more than 25 minutes and the subject had to cooperate in multiple sessions with a total scan time of more than two hours. We have also made efforts in improving image SNR and time-series stability by using a surface coil dedicated to studying human V1, minimizing signal fluctuations by introducing a bite-bar system, making phase and amplitude corrections with a navigator echo acquired at the beginning of each shot, and removing physiological fluctuations from time series (for detail see Cheng et al., 2001). We were eventually convinced that reproducible ODC patterns could indeed be mapped out using this prolonged stimulation 5
paradigm and a differential mapping method (for detail, see Cheng et al., 2001; Cheng, 2012). We were also extremely pleased that some experimental procedures developed in the course of this project were appreciated and later adopted by investigators in other institutions. One annoying problem in this kind of experiments mapping columnar structures is from large surface veins that are not in register spatially with underlying columns. The behaviors of these large surface veins, when imaged using BOLD fMRI, have been described in detail elsewhere (e.g., see Figures 1 and 2 in Cheng, 2012; Menon, 2012). Ideally, these large veins around the regions of interest need to be avoided during image acquisition or identified and removed in the post-processing. Alternatively, experiments may be conducted using other imaging approaches that are less contaminated by large surface veins. For example, Yacoub and colleagues have used the Hahn Spin Echo BOLD fMRI on an ultra high field MRI system (7 Tesla), which is sensitized more to small venous vessels than large surface veins, and reliably map out ODCs in humans (Yacoub et al., 2007).
Imaging other columnar structures in human V1 Revealing other functional structures in V1 using columnar BOLD fMRI has been more challenging, largely due to the fact that, except ODCs that are anatomically segregated, other known functional structures in V1 are formed through complicated intracortical processing. Unlike in studies with experimental animals, invasive techniques, such as single-neuron recording and optical imaging, cannot be used for 6
revealing such functional structures, if present, in human brains. In addition, it is not very easy to find a pair of appropriate stimuli that are both functionally unique and orthogonal to each other (in order to use the differential mapping method), as the attempts of mapping functional architectures using BOLD fMRI with single stimulus conditions have not led to any convincing results in human studies. To readers who are not familiar with this issue, it is important to be reminded that the BOLD signal obtained with a single stimulus condition often contains a large component that is not restricted within home columns. The differential mapping method with a pair of orthogonal stimulus conditions helps remove this component and enhance the differential response that is modulated by the change in the stimulus. For these reasons, though the presence of similar functional structures, such as orientation columns, is expected to exist in human V1, the direct revelation of these structures has not been possible until recently, fittingly, by BOLD fMRI approaches (see below). Inspired by an incidental finding that V1 is almost similarly activated by checker-board patterns whose contrast is reversed at different temporal frequencies (Moradi et al., 2003), we discovered a previously unknown functional structure in human V1 that depends on low or high temporal frequency of visual stimulation (Sun et al., 2007). Reproducibly mapped domains that preferentially respond to higher temporal frequencies appear like isolated islands imbedded in domains that exhibit stronger responses to lower temporal frequencies. Although it is yet to be revealed in monkey V1, the appearance and spatial spacing of these temporal frequency dependent domains suggest that they may be related to the systems of CO patches and interpatches, where 7
neurons are known to differentially respond to stimuli of higher (in patches) and lower (in interpatches) temporal frequencies. This is an interesting hypothesis that can be tested in future. Nevertheless, this study provides an excellent example, demonstrating how imaging studies in humans can complement physiological and imaging studies in experimental animals. In another impressive demonstration of high spatial resolution BOLD fMRI, Essa Yacoub, Noam Harel and Kamil Ugurbil, again using Hahn Spin Echo BOLD fMRI on a 7 Tesla MRI system, with a continuous stimulation paradigm and a phase-mapping analysis method, have successfully mapped out orientation columns in human V1. With the help of certain post-processing of original imaging data, these investigators have reproduced orientation-selective pinwheel structures that are well documented for orientation columns in monkey V1 (Yacoub et al., 2008). High spatial resolution fMRI does not just allow for revelation of the maps of functional architectures. In recent years, we have devised a novel continuous and periodic stimulation paradigm and a differential analysis method for efficiently uncovering orientation-selective responses at the single-voxel (sub-millimeter) level (Sun et al., 2013). Using this approach, we have demonstrated directly the differential BOLD response temporally modulated by the change in orientation and quantitated orientation selectivity for tens of thousands of voxels in human V1. By analyzing orientation preference at a population level and relating orientation preferences of individual voxels to their respective retinotopic locations, we demonstrated for the first time that more voxels prefer horizontal and vertical orientations, a physiological 8
property underlying the oblique effect, referring to the observation that both humans and animals have a greater behavioral sensitivity to the gratings of cardinal (horizontal and vertical) orientations than to the gratings of oblique orientations, and these voxels, respectively, are predominantly located near the cortex representing horizontal and vertical meridians. This observation supports in part the existence of the still hot-debated radial bias in orientation representation and perception. Importantly, it should be emphasized, this study provides clear evidence for the first time that it is the number of voxels, thus number of neurons, and not the strength of responses, that is responsible for the oblique effect. In my opinion, this columnar-resolution approach for studying stimulus-response properties of single voxels is a very useful and powerful way to relate the results obtained from human imaging studies to certain physiological properties known from animal studies, such as representations of colors and motion directions.
Imaging columnar structures in extrastriate cortices Perhaps due to a lack of understanding regarding the spatial specificity of BOLD fMRI and technical demands arisen from conducting experiments of sub-millimeter resolutions (to name a few, for example, low SNR, rigid head motion and physiological noises), collectively, it took the field much longer than originally hoped to convincingly reveal columnar organizations in human V1 using fMRI. It is thus natural to imagine the difficulties of using fMRI to explore functional organizations in extrastriate cortices, as functional differences in functional domains of extrastriate 9
cortices, based on animal studies, tend to be reduced (e.g., in reference to V1 neurons’ differential responses to the stimulation of the two eyes), and some of extrastriate cortical areas are located deep in the cortex, away from the cortical surface, making it harder to achieve higher SNR with surface RF coils, which are typically utilized in high spatial resolution fMRI experiments. Still, in recent years, we have witnessed impressive results mapping functional organizations in extrastriate cortices from a couple of well-conducted studies. In physiological studies in monkey middle temporal (MT) area, it is known that there exists an ordered organization, termed the axis of motion, where groups of neurons preferring a particular direction of motion are often located next to the group of neurons that prefer the opposite direction of motion (Albright et al., 1984; Diogo et al., 2003). The existence of the axis of motion selective features is also expected in human MT complex (hMT+), but it had not been revealed directly. Jan Zimmermann, Rainer Goebel and colleagues have used a 3D gradient and spin echo (GRASE) pulse sequence, a spatial resolution of 0.8 x 0.8 x 0.8 mm3 and a 7 Tesla MRI system, combined with a novel grid sampling analysis method, to map out the organization of axis of motion selective features in hMT+ (Zimmermann et al., 2011). Although the organization of the axis of motion, by definition, is different from (but closely related to) direction-selective hypercolumns (repeated columnar representations of all directions of motion) (Albright et al., 1984) that are yet to be revealed in hMT+, this study represents the first successful attempt in revealing a functional organization in human extrastriate cortices. More recently, Shahin Nasr, Jonathan Polimeni and Roger Tootel have also 10
used a 3D pulse sequence, a spatial resolution of 1 x 1 x 1 mm3 and a 7 Tesla MRI system, and successfully mapped out interdigitated color- and disparity-selective columns in human secondary and tertiary visual cortical areas V2 and V3 (Nasr et al., 2016). In monkeys, V2 contains repeated columnar stripes, which can be anatomically discerned with CO stains. Distinctive “thin” and “thick” stripes of dark CO staining are know to respond selectively to color (in thin stripes) and disparity (in thick stripes) stimuli, respectively. These CO stripes are also present in human V2, and they appear even wider and straighter than ODCs stained with CO for individual eyes (e.g., see Figure 3B in Adams et al., 2007). Given the distinctive features and expected physiological properties of different types of stripes in V2, it is in fact a little surprising that they had not been mapped out using fMRI until this recent study. These investigators deserve to be commended, in particular, for their demonstration that stripes responding selectively to color and disparity stimuli appear abruptly outside the V1/V2 border, and the two types of stripes are different in both width and length.
What we have learned regarding spatial specificity of BOLD fMRI The spatial resolution in an fMRI experiment is ultimately restricted by the SNR, which is proportional to the strength of static magnetic field, voxel size and total scan time. It should be reminded, however, that although high spatial resolution fMRI can help reveal more detail, the spatial resolution per se does not determine the spatial precision of BOLD fMRI. First, the nominal spatial resolution in an fMRI study is not the real resolution of a functional image. As mentioned above, depending on the echo 11
train length and other imaging parameters of the acquisition, T2* blurring can lead to substantial smoothing of an image, as for typical k-space trajectories used in high resolution experiments today, the full width at half maximum (FWHM) of the PSF can be two or more pixels (Jesmanowicz et al., 1998; Kemper et al., 2015). Second, because BOLD fMRI measures summed changes in cerebral blood flow (CBF), cerebral blood volume (CBV) and the cerebral metabolic rate of oxygen (CMRO2) following changes in neuronal activity, the spatial precision of BOLD fMRI is biologically restricted by the underlying vasculature. In an effort to quantify the spatial precision of BOLD fMRI, we have conducted an experiment on a 4 Tesla MRI system, estimating the PSF of BOLD signal using a segmented gradient recalled echo EPI (GRE-EPI) pulse sequence with spatially localized but size-varied stimuli. By measuring the FWHM of the BOLD response profile (from the center of activation to the edge of activation) as a function of the stimulus size, we have found a PSF of approximately 1.8 mm of the BOLD signal in the cortex devoid of large veins running on the cortical surface (for detail, see Figure 1 in Cheng, 2016). This observation is similar to that reported by Shmuel and colleagues in a study conducted using a 7 Tesla MRI system (Shmuel et al., 2007), in which large surface veins were also identified and removed. In general, including large surface veins in the measurements, especially those measured at lower fields, tends to lead to larger PSFs (Engle et al., 1997; Parkes et al., 2005; Olman et al., 2007), in agreement with the theoretical estimate (Turner, 2002). Taken together, these results indicate that the PSF of BOLD fMRI is smaller than 2 mm if the measurement is restricted within the cortex containing on large surface veins. As will be discussed below, this spatial spread of 12
BOLD signal is likely dictated by the underlying microvasculature, which perhaps ultimately defines the upper limit of the spatial precision of BOLD fMRI. This PSF of BOLD fMRI (< 2 mm) is consistent with a large number of experimental observations that it is difficult, if not impossible, to use hemodynamic response based imaging methods, such as intrinsic optical imaging and BOLD fMRI, for mapping functional architectures with any single-condition stimulation paradigms. On the other hand, whenever possible, a differential mapping method, with a pair of complementary stimuli (e.g., the left eye versus the right eye; the horizontal orientation versus the vertical orientation), will allow for revealing selectively increased functional responses at columnar resolution that are in agreement with increased neuronal activity (for a highly relevant animal study showing congruency between functional columns generated using, respectively, BOLD fMRI, intrinsic optical imaging and single-neuron recording, see Moon et al., 2007; Fukuda et al., 2016).
The future of high spatial resolution fMRI MRI technologies in recent years have advanced drastically. The introduction of high-performance RF coils that provide higher SNR, parallel imaging that naturally reduces the echo train length, thus T2* blurring, and accelerated acquisition methods, such as multiband (or simultaneous multi-slice) acquisition approaches that significantly shortens the TR, together with increasingly available ultra high field (7 Tesla and above) MRI systems, makes it possible to routinely conduct high spatial resolution fMRI experiments. In the remaining of this review, I would discuss two 13
issues that I think will have impacts on our understanding how functional architectures in cortical areas, in humans and animals alike, may be organized in relation to the underlying vasculatures and how such functional architectures may be experimentally explored. First, although several important imaging techniques for studying human and non-human primates, including intrinsic optical imaging and BOLD fMRI, depend critically on hemodynamic responses, thus underlying vasculatures, our understanding of these vasculatures and their relationship with neural responses that they support, has been extremely limited. As a matter of fact, our knowledge about cortical vasculatures, in humans in particular, has not advanced much since Duvernoy and his colleagues published their classical paper some 35 years (Duvernoy et al., 1981). In recent years, however, some progresses have been made in macaque V1 and extrastriate cortices (Weber et al., 2008; Keller et al., 2011; Adams et al., 2015; see also Zheng et al., 1991) as well as cat visual cortex (Bolan et al., 2006). In an elegantly conducted anatomical study, Adams and colleagues (2015) compared the patterns of macaque CO patches and interpatches and microvascular lobules (or neurovascular units termed by Duvernoy and colleagues (1981); see Figure 1), and found that though the two systems bore no mutual relationship spatially, they had a similar periodicity, implying that they resemble each other in size and total number per unit area in cortex: both CO patches and microvascular lobules are round in shape, and there is one CO patch per 0.20 mm2 cortex and one microvascular lobule per 0.22 mm2 cortex (Figure 1 C and D). These quantitative measures are of extreme interest. In humans, the size of microvascular 14
lobule (i.e., neurovascular unit) in V1 is not known, but its diameter measured from other part of cortex appears approximately twice larger than that in macaque V1 (Figure 1 A and B). In both macaques and humans, the ratio of cortical area occupied by CO patches to that occupied by interpatches is ~1:2, and CO patches are located in the centers of ODCs (Adams et al., 2007, 2015). Because the width of human ODCs is approximately twice bigger than that of macaque ODCs, if there is also a roughly one to one correspondence, size wise, between neurovascular unit and CO patch in humans, the size of neurovascular unit in human V1 should be similar to that shown in Figure 1B. This idea can be tested. Though it is still demanding to image fine cortical vessels in humans, several studies have shown that it is possible to reveal them in vivo at the levels of individual arterioles and venules (Cho et al., 2008; Lee et al., 2015). As the PSF of BOLD fMRI is thought to be restricted by the underlying vasculature, namely, the neurovascular unit, quantifying its size and spacing, in human V1 and beyond, as well as their differences between brain areas, should undoubtedly help us better understand the spatial precision of this increasingly important imaging technique. Finally, I will give some of my thoughts on the choice of imaging resolution in relation to the organization of columnar structures to be studied. In a recent BOLD fMRI study of hue selectivity in early visual cortical areas (V1-V4) in humans, we found that voxel-wise color selective responses can be reliably revealed, even though the voxel size that we used was relatively big (2 x 2 x 3 mm3, see Kuriki et al., 2015). This is quite impressive, indeed, as color maps in macaque V1 and V2 are organized in sub-millimeter domains, and the color domain in V4 is slightly bigger, measuring 15
approximately 1 mm in size (Figure 2 A-C). It is thought that similar color maps (hereafter they are referred to as color domains/columns, as they generally preserve the features of columns: color preference change gradually across cortical surface but is relatively stable across cortical layers) also exist in human visual cortex, but at present, there is no means to directly visualize them. Though the domains/columns in humans can be twice larger than those in macaques, they are still much finer than the typical resolution that today’s fMRI can offer. We reasoned that a biased color preference can still be revealed even at a conventional resolution of 2 x 2 x 3 mm3. In the simplified model shown in Figure 2D, the gradual change in color preference across cortical surface is illustrated only in one dimension, and a single voxel can contain color domains of multiple patches in V1, multiple thin stripes in V2 or a restricted color domain in V4, albeit in reality these domains can take varied shapes and sizes, and their color preferences can change in two dimensions. This model can readily predict that a voxel of conventional resolution is selective for a color if the domains/columns coding the color are excessively contained in the same voxel. This interpretation resembles that offered to explain why fine stimulus attributes, such as orientation selective responses that are represented in orientation columns, can be decoded using a voxel whose size is much bigger than the size of orientation columns (Boynton, 2005). In our case, however, instead of the mysterious small bias in responses, we observed significant color selective responses in substantial portions of voxels in all subjects and visual areas studied (on average, 23% in V1, 22% in V2, 21% in V3 and 18% in V4, see Supplementary Materials to Kuriki et al., 2015). In other two studies conducted at the 16
columnar resolution, we also observed significant selective responses for eyes (~25% in Cheng et al., 2001) and stimulus orientations (~35% in Sun et al., 2013). The proportion of significantly responsive voxels does not appear to rely on the spatial resolution used in an experiment; rather, it seems that there is an interrelated relationship between the spatial resolution and the size of underlying functional architectures, which may be uncovered using a multi-resolution approach. This is a hypothesis that is worth being tested in future. In conclusion, I believe that high-resolution fMRI, especially the experiments conducted using ultra high field MRI systems, will play pivotal roles in helping us better understand functional organizations in human extrastriate cortices, such as categorical representations of visual objects in inferotemporal cortex, in exploring physiological properties of sub-systems related to color and motion processing, and ultimately, in elucidating how neuronal units and vascular units are related to each other and are organized across different brain regions.
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Acknowledgments I am indebted to my colleagues, Keiji Tanaka, R. Allen Waggoner, and Kenichi Ueno, for their support and to Pei Sun, Justin L. Gardner, Mauro Costagli, Ichiro Kuriki, Topi Tanskanen, and Chien-Hui Kao for their hard work in a number of painstaking studies. Preparation of this manuscript was partially supported by a grant from the Japan Society of the Promotion of Sciences (JSPS; grant 25242079) (to K.C.) and by the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and Development (AMED) (to K.C.).
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Figure legends Figure 1. Cortical vasculatures in an unidentified human brain area (A) and macaque striate cortex (V1) (C). Circumscribed areas in A and C, indicated by green rectangles, are magnified in B and D, respectively, which exemplify a neurovascular unit (B, see Duvernoy et al., 1981) or a microvascular lobule (D, see Adams et al., 2015). A neurovascular unit/microvascular lobule is composed of an emerging venule (larger blue circle in B and D) and a ring of penetrating arterioles (smaller red circles in B and D). Note the difference between scales in B and D; a neurovascular unit in this part of human cortex serves much larger area than a microvascular lobule in macaque V1 does. A and B were modified from Duvernoy et al., (1981) and C and D were modified from Adams et al., (2015). Figure 2. Color-selective maps revealed using intrinsic optical imaging in macaque V1 (A), V2 (B) and V4 (C). Each colored contour/region represents a domain that is preferentially activated by that color. Neighboring domains overlap substantially, but their peaks shift across cortex systematically. These maps, though are at different spatial scales, demonstrate a systematic shift in color preference across the cytochrome oxidase (CO) patches in V1 (color preference changes every tens of microns; for color domain and CO patch relationship, see Lu and Roe, 2008), CO thin stripes in V2 (entire spectrum of colors is represented within approximately 0.5 mm) and discrete color-selective regions in V4 (different colors are separated by >0.5 mm). D illustrates an imagined resulted preferred color when such color-selective maps in macaque V1, V2 or V4 is imaged using BOLD fMRI with a voxel that is much larger than the 19
domains representing an entire set of colors. A was adopted from Xiao et al., (2007; see their Figure 4A), B was adopted from Wang et al., (2007; see their Figure 9), and C was adopted from Tanigawa et al., (2010; see their Figure 6e, bottom panel).
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understanding local neural processing. Progress Brain Res. 225, 123-152. Fukushima, E., Roeder, S.B.W., 1981. Experimental pulse NMR : a nuts and bolts approach. Addison-Wesley Pub. Co., Advanced Book Program, Reading, Mass. Goodyear, B.G., Menon, R.S., 2001. Brief visual stimulation allows mapping of ocular dominance in visual cortex using fMRI. Hum. Brain Mapp. 14, 210-217. Grinvald, A., Slovin, H., Vanzetta, I., 2000. Non-invasive visualization of cortical columns by fMRI. Nat. Neurosci. 3, 105-107. Horton, J.C., 1984. Cytochrome oxidase patches: a new cytoarchitectonic feature of monkey visual cortex. Philos. Trans. R. Soc. Lond. Biol. B 304, 199-253. Horton, J.C., Dagi, L.R., McCrane, E.P., de Monasterio, F.M., 1990. Arrangement of ocular dominance columns in human visual cortex. Arch. Ophthalmol. 108, 1025-1031. Horton, J.C., Hedley-Whyne, E.T., 1984. Mapping of cytochrome oxidase patches and ocular dominance columns in human visual cortex. Philos. Trans. R. Soc. Lond. Biol. B 304, 255-272. Hubel, D.H., Wiesel, T.N., 1972. Laminar and columnar distribution of geniculo-cortical fibers in macaque monkeys. J. Comp. Neurol. 146, 421-450. Jesmanowicz, A., Bandettini, P.A., Hyde, J.S., 1998. Single-shot half k-space high-resolution gradient-recalled EPI for fMRI at 3 Tesla. Magn. Reson. Med. 40, 754-762. Keller, A.L., Schuz, A., Logothetis, N.K., Weber, B., 2011. Vascularization of cytochrome oxidase-rich blobs in the primary visual cortex of squirrel and macaque monkeys. J. Neurosci. 31, 1246-1253. Kemper V.G., De Martino, F., Vu, A.T., Poser, B.A., Feinberg, D.A., Goebel, R., Yacoub, E., 2015. Sub-millimeter T2 weighted fMRI at 7 T: comparison of 3D-GRASE and 2D SE-EPI. Frontiers in Neuroscience 9, Article 163, 1-14. Kuriki, I., Sun, P., Ueno, K., Tanaka, K., Cheng, K., 2015. Hue selectivity in human visual cortex revealed by functional magnetic resonance imaging. Cereb. Cortex 25, 4869-4884. Lee, Y.-B., Seo, Y.-B., Park, C.-A., Kang, C.-K., 2015. Imaging method for changes in venous dynamics: a preliminary study. NeuroReport 26, 333-340. Lu, H.D., Roe, A.W., 2008. Functional organization og color domains in V1 and V2 of macaque monkey revealed by optical imaging. Cereb. Cortex 18, 516-533. Malonek, D., Grinvald, A., 1996. Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 272, 551-554. Menon, R.S., 2012. The great brain versus vein debate. NeuroImage 62, 970-974.
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Wang, Y., Xiao, Y., Felleman, D.J., 2007. V2 thin stripes contain spatially organized representations of achoromatic luminance change. Cereb. Cortex 17, 116-129. Weber, B., Keller, A.L., Reichold, J., Logothetis, N.K., 2008. The microvascular system of the striate and extrastriate visual cortex of the macaque. Cereb. Cortex 18, 2318-2330. Wiesel, T.N., Hubel, D.H., Lam, D.M.K., 1974. Autoradiographic demonstration of ocular dominance columns in the monkey striate cortex by means of transneuronal transport. Brain Res. 79, 273-279. Xiao, Y., Casti, A., Xiao, J., Kaplan, E., 2007. Hue maps in primate striate cortex. NeuroImage 35, 771-786. Yacoub, E., Harel, N., Ugurbil, K., 2008. High-field fMRI unveils orientation columns in humans. Proc. Natl. Acad. Sci. U S A 105, 10607-10612. Yacoub, E., Shmuel, A., Logothetis, N., Ugurbil, K., 2007. Robust detection of ocular dominance columns in humans using Hahn Spin Echo BOLD functional MRI at 7 Tesla. NeuroImage 37, 1161-1177. Zheng, D., LaMantia, A.S., Purves, D., 1991. Specialized vascularization of the primate visual cortex. J. Neurosci. 11, 2622-2629. Zimmermann, J., Goebel, R., De Martino, F., van de Moortele, P.F., Feinberg, D., Adriany, G., Chaimow, D., Shmuel, A., Ugurbil, K., Yacoub, E., 2011. Mapping the organization of axis of motion selective features in human area MT using high-field fMRI. PLoS One 6, e28716.
Highlights
Several columnar structures are revealed in human V1 using high-resolution BOLD fMRI
Motion, color and disparity selective structures are found in human MT, and V2 and V3
High-resolution fMRI can help bridge human imaging and animal physiological studies
High-resoution fMRI helps understand how cortical microvasculature may be organized
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PII: DOI: Reference:
S1053-8119(16)30638-3 http://dx.doi.org/10.1016/j.neuroimage.2016.11.018 YNIMG13566
To appear in: NeuroImage Received date: 29 August 2016 Accepted date: 8 November 2016 Cite this article as: Kang cheng, Exploration of Human Visual Cortex Using High Spatial Resolution Functional Magnetic Resonance Imaging, NeuroImage, http://dx.doi.org/10.1016/j.neuroimage.2016.11.018 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Exploration of Human Visual Cortex Using High Spatial Resolution Functional Magnetic Resonance Imaging Kang Cheng Support Unit for Functional Magnetic Resonance Imaging, and Laboratory for Cognitive Brain Mapping RIKEN Brain Science Institute 2-1 Hirosawa, Wako, Saitama 351-0198, Japan Tel: +81 48 467 9730; E-mail: [email protected] Abstract In this review focusing primarily on the work conducted in my group at RIKEN Brain Science Institute, I will first briefly summarize what we have achieved in mapping columnar organizations in human primary visual cortex using blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI), including ocular dominance columns, temporal frequency dependent domains, and orientation selective columns. I will then touch upon a couple of recent successful attempts in the field in mapping functional architectures in human extrastriate cortices, including human middle temporal complex and secondary and tertiary visual areas (V2 and V3), and discuss what we have learned regarding the spatial specificity of BOLD fMRI. Finally, I will offer some of my personal thoughts on how functional architectures may be organized in relation to underlying microvasculature and how such functional architectures may be experimentally explored.
Keywords: High spatial resolution fMRI, human, striate cortex (V1), extrastraite cortex, pointe spread function, microvasculature 1
Imaging ocular dominance columns in human primary visual cortex Ever since the inception of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging (fMRI), its spatial specificity has always been the concern as well as the hope. Some of the earliest efforts have been made in order to use BOLD fMRI to map functional architectures in human visual cortex, in particular, columnar structures. Indeed, several sites that owned 4 Tesla whole-body MRI systems in the middle of the 1990s, including the Center for Magnetic Resonance Research, University of Minnesota in the United States, the Robarts Research Institute, the University of Western Ontario in Canada, and RIKEN Brain Science Institute in Japan, were all involved in mapping human ocular dominance columns (ODCs). The combined efforts in these studies, some of which are briefly reviewed below, have greatly advanced our understanding of the spatial specificity of BOLD fMRI, and have helped in establishing certain techniques and practical experimental procedures for future exploration of columnar structures in human visual cortex and beyond. The system of ODCs is one of the clearest examples of the distinct functional architecture within the cortex. Neurons in the primary visual cortex (striate cortex, V1) receive input primarily from either the left eye or right eye and are grouped in periodic left-right eye stripes on the cortical surface (or slabs if viewed through the cortical thickness) that run roughly parallel to the equi-eccentricity representations. Although neurons are less purely monocular in the upper and lower layers, the dominance by one eye or the opposite eye is consistent throughout six cortical layers. Originally discovered in cats and monkeys using the single-neuron recording technique, the spatial layout of 2
ODCs was subsequently revealed using several anatomical approaches, including autoradiographic demonstration of ODCs in monkey V1 by means of transneuronal transport after the tracer was injected into one eye (Hubel and Wiesel, 1972; Wiesel et al., 1974), and revelation of the ODC pattern with cytochrome oxidase (CO) stain, which selectively showed reduced oxidative activity in ODCs corresponding to the eye that was experimentally removed (Horton, 1984), and later mapped out with intrinsic optical imaging methods (Blasdel and Salama, 1986; Ts’o et al., 1990). Importantly, ODCs, to the best of our knowledge so far, are the only hardwired system in visual cortex, as monocular geniculocortical afferents serving the two eyes are strictly segregated in V1. ODCs in humans resemble those in monkeys. In a few rare cases, the results from human post-mortem brains stained with CO demonstrate that ODCs in humans, with a similar layout as that in monkeys, measure approximately 1 mm in width, which is roughly twice as wide as the width of ODCs in monkeys (Horton and Hedley-Whyne, 1984; Horton et al., 1990; Adams et al., 2007). Human ODCs, thus, provide a perfect model for testing the spatial resolvability of BOLD fMRI. Ravi Menon, when he was still at the University of Minnesota, first showed that ocular dominance, that is, the differential response to the photic stimulation of either the left eye or the right eye, could be revealed in humans using BOLD fMRI (Menon et al., 1997). Later, after moving to the University of Western Ontario, Ravi and his student Bradley Goodyear, further showed that ODC patterns in individual subjects could be mapped out, suggesting that BOLD fMRI may possess a point spread function (PSF) that is fine enough to resolve functional structures at columnar resolution (Menon and 3
Goodyear, 1999). At about the same time, Peter Dechent and Jens Frahm also published a paper, using a 2 Tesla scanner and a FLASH pulse sequence, showing that ODCs in humans can be directly mapped out (Dechent and Frahm, 2000). We commenced our ODC project relatively late, in hope, primarily, to develop high spatial resolution fMRI techniques through this project for imaging columnar structures in extrastriate cortices. The technologies at that time, compared with what are available today, were rather primitive, as most of our experiments were conducted using a single-loop or a two-loop quadrature RF coil with a single reception channel. Perhaps because we were extremely concerned with the T2* blurring of images acquired using BOLD fMRI (one of my close collaborators, our MR physicist, R. Allen Waggoner, was trained by Dr. Eiichi Fukushima, famous for his “MR Nuts and Bolts” book (Fukushima and Roeder, 1981), in which the issue of T2* blurring was clearly described), in order to shorten the echo train length and achieve a relatively high signal to noise ratio (SNR), we used a multi-shot echo-planar imaging (EPI) pulse sequence, which made the volume TR very long even for acquiring a small number of slices (9.6 s with three interleaved slices). This was a problem, as this would make it extremely difficult to capture the early positive BOLD responses following visual stimulation, which are spatially more localized (Goodyear and Menon, 2001). At that time, influenced largely by works conducted using intrinsic optical imaging, which, similar to BOLD fMRI, relies on hemodynamic responses as mapping signals, it was commonly believed that the spatial specificity might be lost if later positive responses are used as a mapping signal. This is true if responses from a single condition (e.g., left-eye stimulation versus baseline) are used as a mapping 4
signal. However, we had noticed then, from several published intrinsic optical imaging papers (e.g., Malonek and Grinvald, 1996; Grinvald et al., 2000), as well as observations from our own optical imaging experiments, that the differential mapping signal (i.e., the subtraction between images acquired when the left eye is stimulated and those acquired when the right eye is stimulated) actually preserves a consistent spatially resolved pattern for an extended period, even after the stimulation is switched off (this is now well established; for explanation and demonstration of this statement, see Figure 2 and legends in an excellent recent review by Fukuda and colleagues, 2016). Faced with the dilemma of likely reduced differential responses to the stimulation of the two eyes when the stimulation is prolonged, but potentially increased image SNR through averaging of larger number of images, we decided to run our experiments using a prolonged stimulation paradigm (this was, in a sense, a desperate choice; each eye each time was stimulated for up to two minutes). Even to this day, I still owe my heartfelt thanks to several of my dedicated subjects for their efforts in participating in repeated experiments, in which each single session would last more than 25 minutes and the subject had to cooperate in multiple sessions with a total scan time of more than two hours. We have also made efforts in improving image SNR and time-series stability by using a surface coil dedicated to studying human V1, minimizing signal fluctuations by introducing a bite-bar system, making phase and amplitude corrections with a navigator echo acquired at the beginning of each shot, and removing physiological fluctuations from time series (for detail see Cheng et al., 2001). We were eventually convinced that reproducible ODC patterns could indeed be mapped out using this prolonged stimulation 5
paradigm and a differential mapping method (for detail, see Cheng et al., 2001; Cheng, 2012). We were also extremely pleased that some experimental procedures developed in the course of this project were appreciated and later adopted by investigators in other institutions. One annoying problem in this kind of experiments mapping columnar structures is from large surface veins that are not in register spatially with underlying columns. The behaviors of these large surface veins, when imaged using BOLD fMRI, have been described in detail elsewhere (e.g., see Figures 1 and 2 in Cheng, 2012; Menon, 2012). Ideally, these large veins around the regions of interest need to be avoided during image acquisition or identified and removed in the post-processing. Alternatively, experiments may be conducted using other imaging approaches that are less contaminated by large surface veins. For example, Yacoub and colleagues have used the Hahn Spin Echo BOLD fMRI on an ultra high field MRI system (7 Tesla), which is sensitized more to small venous vessels than large surface veins, and reliably map out ODCs in humans (Yacoub et al., 2007).
Imaging other columnar structures in human V1 Revealing other functional structures in V1 using columnar BOLD fMRI has been more challenging, largely due to the fact that, except ODCs that are anatomically segregated, other known functional structures in V1 are formed through complicated intracortical processing. Unlike in studies with experimental animals, invasive techniques, such as single-neuron recording and optical imaging, cannot be used for 6
revealing such functional structures, if present, in human brains. In addition, it is not very easy to find a pair of appropriate stimuli that are both functionally unique and orthogonal to each other (in order to use the differential mapping method), as the attempts of mapping functional architectures using BOLD fMRI with single stimulus conditions have not led to any convincing results in human studies. To readers who are not familiar with this issue, it is important to be reminded that the BOLD signal obtained with a single stimulus condition often contains a large component that is not restricted within home columns. The differential mapping method with a pair of orthogonal stimulus conditions helps remove this component and enhance the differential response that is modulated by the change in the stimulus. For these reasons, though the presence of similar functional structures, such as orientation columns, is expected to exist in human V1, the direct revelation of these structures has not been possible until recently, fittingly, by BOLD fMRI approaches (see below). Inspired by an incidental finding that V1 is almost similarly activated by checker-board patterns whose contrast is reversed at different temporal frequencies (Moradi et al., 2003), we discovered a previously unknown functional structure in human V1 that depends on low or high temporal frequency of visual stimulation (Sun et al., 2007). Reproducibly mapped domains that preferentially respond to higher temporal frequencies appear like isolated islands imbedded in domains that exhibit stronger responses to lower temporal frequencies. Although it is yet to be revealed in monkey V1, the appearance and spatial spacing of these temporal frequency dependent domains suggest that they may be related to the systems of CO patches and interpatches, where 7
neurons are known to differentially respond to stimuli of higher (in patches) and lower (in interpatches) temporal frequencies. This is an interesting hypothesis that can be tested in future. Nevertheless, this study provides an excellent example, demonstrating how imaging studies in humans can complement physiological and imaging studies in experimental animals. In another impressive demonstration of high spatial resolution BOLD fMRI, Essa Yacoub, Noam Harel and Kamil Ugurbil, again using Hahn Spin Echo BOLD fMRI on a 7 Tesla MRI system, with a continuous stimulation paradigm and a phase-mapping analysis method, have successfully mapped out orientation columns in human V1. With the help of certain post-processing of original imaging data, these investigators have reproduced orientation-selective pinwheel structures that are well documented for orientation columns in monkey V1 (Yacoub et al., 2008). High spatial resolution fMRI does not just allow for revelation of the maps of functional architectures. In recent years, we have devised a novel continuous and periodic stimulation paradigm and a differential analysis method for efficiently uncovering orientation-selective responses at the single-voxel (sub-millimeter) level (Sun et al., 2013). Using this approach, we have demonstrated directly the differential BOLD response temporally modulated by the change in orientation and quantitated orientation selectivity for tens of thousands of voxels in human V1. By analyzing orientation preference at a population level and relating orientation preferences of individual voxels to their respective retinotopic locations, we demonstrated for the first time that more voxels prefer horizontal and vertical orientations, a physiological 8
property underlying the oblique effect, referring to the observation that both humans and animals have a greater behavioral sensitivity to the gratings of cardinal (horizontal and vertical) orientations than to the gratings of oblique orientations, and these voxels, respectively, are predominantly located near the cortex representing horizontal and vertical meridians. This observation supports in part the existence of the still hot-debated radial bias in orientation representation and perception. Importantly, it should be emphasized, this study provides clear evidence for the first time that it is the number of voxels, thus number of neurons, and not the strength of responses, that is responsible for the oblique effect. In my opinion, this columnar-resolution approach for studying stimulus-response properties of single voxels is a very useful and powerful way to relate the results obtained from human imaging studies to certain physiological properties known from animal studies, such as representations of colors and motion directions.
Imaging columnar structures in extrastriate cortices Perhaps due to a lack of understanding regarding the spatial specificity of BOLD fMRI and technical demands arisen from conducting experiments of sub-millimeter resolutions (to name a few, for example, low SNR, rigid head motion and physiological noises), collectively, it took the field much longer than originally hoped to convincingly reveal columnar organizations in human V1 using fMRI. It is thus natural to imagine the difficulties of using fMRI to explore functional organizations in extrastriate cortices, as functional differences in functional domains of extrastriate 9
cortices, based on animal studies, tend to be reduced (e.g., in reference to V1 neurons’ differential responses to the stimulation of the two eyes), and some of extrastriate cortical areas are located deep in the cortex, away from the cortical surface, making it harder to achieve higher SNR with surface RF coils, which are typically utilized in high spatial resolution fMRI experiments. Still, in recent years, we have witnessed impressive results mapping functional organizations in extrastriate cortices from a couple of well-conducted studies. In physiological studies in monkey middle temporal (MT) area, it is known that there exists an ordered organization, termed the axis of motion, where groups of neurons preferring a particular direction of motion are often located next to the group of neurons that prefer the opposite direction of motion (Albright et al., 1984; Diogo et al., 2003). The existence of the axis of motion selective features is also expected in human MT complex (hMT+), but it had not been revealed directly. Jan Zimmermann, Rainer Goebel and colleagues have used a 3D gradient and spin echo (GRASE) pulse sequence, a spatial resolution of 0.8 x 0.8 x 0.8 mm3 and a 7 Tesla MRI system, combined with a novel grid sampling analysis method, to map out the organization of axis of motion selective features in hMT+ (Zimmermann et al., 2011). Although the organization of the axis of motion, by definition, is different from (but closely related to) direction-selective hypercolumns (repeated columnar representations of all directions of motion) (Albright et al., 1984) that are yet to be revealed in hMT+, this study represents the first successful attempt in revealing a functional organization in human extrastriate cortices. More recently, Shahin Nasr, Jonathan Polimeni and Roger Tootel have also 10
used a 3D pulse sequence, a spatial resolution of 1 x 1 x 1 mm3 and a 7 Tesla MRI system, and successfully mapped out interdigitated color- and disparity-selective columns in human secondary and tertiary visual cortical areas V2 and V3 (Nasr et al., 2016). In monkeys, V2 contains repeated columnar stripes, which can be anatomically discerned with CO stains. Distinctive “thin” and “thick” stripes of dark CO staining are know to respond selectively to color (in thin stripes) and disparity (in thick stripes) stimuli, respectively. These CO stripes are also present in human V2, and they appear even wider and straighter than ODCs stained with CO for individual eyes (e.g., see Figure 3B in Adams et al., 2007). Given the distinctive features and expected physiological properties of different types of stripes in V2, it is in fact a little surprising that they had not been mapped out using fMRI until this recent study. These investigators deserve to be commended, in particular, for their demonstration that stripes responding selectively to color and disparity stimuli appear abruptly outside the V1/V2 border, and the two types of stripes are different in both width and length.
What we have learned regarding spatial specificity of BOLD fMRI The spatial resolution in an fMRI experiment is ultimately restricted by the SNR, which is proportional to the strength of static magnetic field, voxel size and total scan time. It should be reminded, however, that although high spatial resolution fMRI can help reveal more detail, the spatial resolution per se does not determine the spatial precision of BOLD fMRI. First, the nominal spatial resolution in an fMRI study is not the real resolution of a functional image. As mentioned above, depending on the echo 11
train length and other imaging parameters of the acquisition, T2* blurring can lead to substantial smoothing of an image, as for typical k-space trajectories used in high resolution experiments today, the full width at half maximum (FWHM) of the PSF can be two or more pixels (Jesmanowicz et al., 1998; Kemper et al., 2015). Second, because BOLD fMRI measures summed changes in cerebral blood flow (CBF), cerebral blood volume (CBV) and the cerebral metabolic rate of oxygen (CMRO2) following changes in neuronal activity, the spatial precision of BOLD fMRI is biologically restricted by the underlying vasculature. In an effort to quantify the spatial precision of BOLD fMRI, we have conducted an experiment on a 4 Tesla MRI system, estimating the PSF of BOLD signal using a segmented gradient recalled echo EPI (GRE-EPI) pulse sequence with spatially localized but size-varied stimuli. By measuring the FWHM of the BOLD response profile (from the center of activation to the edge of activation) as a function of the stimulus size, we have found a PSF of approximately 1.8 mm of the BOLD signal in the cortex devoid of large veins running on the cortical surface (for detail, see Figure 1 in Cheng, 2016). This observation is similar to that reported by Shmuel and colleagues in a study conducted using a 7 Tesla MRI system (Shmuel et al., 2007), in which large surface veins were also identified and removed. In general, including large surface veins in the measurements, especially those measured at lower fields, tends to lead to larger PSFs (Engle et al., 1997; Parkes et al., 2005; Olman et al., 2007), in agreement with the theoretical estimate (Turner, 2002). Taken together, these results indicate that the PSF of BOLD fMRI is smaller than 2 mm if the measurement is restricted within the cortex containing on large surface veins. As will be discussed below, this spatial spread of 12
BOLD signal is likely dictated by the underlying microvasculature, which perhaps ultimately defines the upper limit of the spatial precision of BOLD fMRI. This PSF of BOLD fMRI (< 2 mm) is consistent with a large number of experimental observations that it is difficult, if not impossible, to use hemodynamic response based imaging methods, such as intrinsic optical imaging and BOLD fMRI, for mapping functional architectures with any single-condition stimulation paradigms. On the other hand, whenever possible, a differential mapping method, with a pair of complementary stimuli (e.g., the left eye versus the right eye; the horizontal orientation versus the vertical orientation), will allow for revealing selectively increased functional responses at columnar resolution that are in agreement with increased neuronal activity (for a highly relevant animal study showing congruency between functional columns generated using, respectively, BOLD fMRI, intrinsic optical imaging and single-neuron recording, see Moon et al., 2007; Fukuda et al., 2016).
The future of high spatial resolution fMRI MRI technologies in recent years have advanced drastically. The introduction of high-performance RF coils that provide higher SNR, parallel imaging that naturally reduces the echo train length, thus T2* blurring, and accelerated acquisition methods, such as multiband (or simultaneous multi-slice) acquisition approaches that significantly shortens the TR, together with increasingly available ultra high field (7 Tesla and above) MRI systems, makes it possible to routinely conduct high spatial resolution fMRI experiments. In the remaining of this review, I would discuss two 13
issues that I think will have impacts on our understanding how functional architectures in cortical areas, in humans and animals alike, may be organized in relation to the underlying vasculatures and how such functional architectures may be experimentally explored. First, although several important imaging techniques for studying human and non-human primates, including intrinsic optical imaging and BOLD fMRI, depend critically on hemodynamic responses, thus underlying vasculatures, our understanding of these vasculatures and their relationship with neural responses that they support, has been extremely limited. As a matter of fact, our knowledge about cortical vasculatures, in humans in particular, has not advanced much since Duvernoy and his colleagues published their classical paper some 35 years (Duvernoy et al., 1981). In recent years, however, some progresses have been made in macaque V1 and extrastriate cortices (Weber et al., 2008; Keller et al., 2011; Adams et al., 2015; see also Zheng et al., 1991) as well as cat visual cortex (Bolan et al., 2006). In an elegantly conducted anatomical study, Adams and colleagues (2015) compared the patterns of macaque CO patches and interpatches and microvascular lobules (or neurovascular units termed by Duvernoy and colleagues (1981); see Figure 1), and found that though the two systems bore no mutual relationship spatially, they had a similar periodicity, implying that they resemble each other in size and total number per unit area in cortex: both CO patches and microvascular lobules are round in shape, and there is one CO patch per 0.20 mm2 cortex and one microvascular lobule per 0.22 mm2 cortex (Figure 1 C and D). These quantitative measures are of extreme interest. In humans, the size of microvascular 14
lobule (i.e., neurovascular unit) in V1 is not known, but its diameter measured from other part of cortex appears approximately twice larger than that in macaque V1 (Figure 1 A and B). In both macaques and humans, the ratio of cortical area occupied by CO patches to that occupied by interpatches is ~1:2, and CO patches are located in the centers of ODCs (Adams et al., 2007, 2015). Because the width of human ODCs is approximately twice bigger than that of macaque ODCs, if there is also a roughly one to one correspondence, size wise, between neurovascular unit and CO patch in humans, the size of neurovascular unit in human V1 should be similar to that shown in Figure 1B. This idea can be tested. Though it is still demanding to image fine cortical vessels in humans, several studies have shown that it is possible to reveal them in vivo at the levels of individual arterioles and venules (Cho et al., 2008; Lee et al., 2015). As the PSF of BOLD fMRI is thought to be restricted by the underlying vasculature, namely, the neurovascular unit, quantifying its size and spacing, in human V1 and beyond, as well as their differences between brain areas, should undoubtedly help us better understand the spatial precision of this increasingly important imaging technique. Finally, I will give some of my thoughts on the choice of imaging resolution in relation to the organization of columnar structures to be studied. In a recent BOLD fMRI study of hue selectivity in early visual cortical areas (V1-V4) in humans, we found that voxel-wise color selective responses can be reliably revealed, even though the voxel size that we used was relatively big (2 x 2 x 3 mm3, see Kuriki et al., 2015). This is quite impressive, indeed, as color maps in macaque V1 and V2 are organized in sub-millimeter domains, and the color domain in V4 is slightly bigger, measuring 15
approximately 1 mm in size (Figure 2 A-C). It is thought that similar color maps (hereafter they are referred to as color domains/columns, as they generally preserve the features of columns: color preference change gradually across cortical surface but is relatively stable across cortical layers) also exist in human visual cortex, but at present, there is no means to directly visualize them. Though the domains/columns in humans can be twice larger than those in macaques, they are still much finer than the typical resolution that today’s fMRI can offer. We reasoned that a biased color preference can still be revealed even at a conventional resolution of 2 x 2 x 3 mm3. In the simplified model shown in Figure 2D, the gradual change in color preference across cortical surface is illustrated only in one dimension, and a single voxel can contain color domains of multiple patches in V1, multiple thin stripes in V2 or a restricted color domain in V4, albeit in reality these domains can take varied shapes and sizes, and their color preferences can change in two dimensions. This model can readily predict that a voxel of conventional resolution is selective for a color if the domains/columns coding the color are excessively contained in the same voxel. This interpretation resembles that offered to explain why fine stimulus attributes, such as orientation selective responses that are represented in orientation columns, can be decoded using a voxel whose size is much bigger than the size of orientation columns (Boynton, 2005). In our case, however, instead of the mysterious small bias in responses, we observed significant color selective responses in substantial portions of voxels in all subjects and visual areas studied (on average, 23% in V1, 22% in V2, 21% in V3 and 18% in V4, see Supplementary Materials to Kuriki et al., 2015). In other two studies conducted at the 16
columnar resolution, we also observed significant selective responses for eyes (~25% in Cheng et al., 2001) and stimulus orientations (~35% in Sun et al., 2013). The proportion of significantly responsive voxels does not appear to rely on the spatial resolution used in an experiment; rather, it seems that there is an interrelated relationship between the spatial resolution and the size of underlying functional architectures, which may be uncovered using a multi-resolution approach. This is a hypothesis that is worth being tested in future. In conclusion, I believe that high-resolution fMRI, especially the experiments conducted using ultra high field MRI systems, will play pivotal roles in helping us better understand functional organizations in human extrastriate cortices, such as categorical representations of visual objects in inferotemporal cortex, in exploring physiological properties of sub-systems related to color and motion processing, and ultimately, in elucidating how neuronal units and vascular units are related to each other and are organized across different brain regions.
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Acknowledgments I am indebted to my colleagues, Keiji Tanaka, R. Allen Waggoner, and Kenichi Ueno, for their support and to Pei Sun, Justin L. Gardner, Mauro Costagli, Ichiro Kuriki, Topi Tanskanen, and Chien-Hui Kao for their hard work in a number of painstaking studies. Preparation of this manuscript was partially supported by a grant from the Japan Society of the Promotion of Sciences (JSPS; grant 25242079) (to K.C.) and by the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from Japan Agency for Medical Research and Development (AMED) (to K.C.).
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Figure legends Figure 1. Cortical vasculatures in an unidentified human brain area (A) and macaque striate cortex (V1) (C). Circumscribed areas in A and C, indicated by green rectangles, are magnified in B and D, respectively, which exemplify a neurovascular unit (B, see Duvernoy et al., 1981) or a microvascular lobule (D, see Adams et al., 2015). A neurovascular unit/microvascular lobule is composed of an emerging venule (larger blue circle in B and D) and a ring of penetrating arterioles (smaller red circles in B and D). Note the difference between scales in B and D; a neurovascular unit in this part of human cortex serves much larger area than a microvascular lobule in macaque V1 does. A and B were modified from Duvernoy et al., (1981) and C and D were modified from Adams et al., (2015). Figure 2. Color-selective maps revealed using intrinsic optical imaging in macaque V1 (A), V2 (B) and V4 (C). Each colored contour/region represents a domain that is preferentially activated by that color. Neighboring domains overlap substantially, but their peaks shift across cortex systematically. These maps, though are at different spatial scales, demonstrate a systematic shift in color preference across the cytochrome oxidase (CO) patches in V1 (color preference changes every tens of microns; for color domain and CO patch relationship, see Lu and Roe, 2008), CO thin stripes in V2 (entire spectrum of colors is represented within approximately 0.5 mm) and discrete color-selective regions in V4 (different colors are separated by >0.5 mm). D illustrates an imagined resulted preferred color when such color-selective maps in macaque V1, V2 or V4 is imaged using BOLD fMRI with a voxel that is much larger than the 19
domains representing an entire set of colors. A was adopted from Xiao et al., (2007; see their Figure 4A), B was adopted from Wang et al., (2007; see their Figure 9), and C was adopted from Tanigawa et al., (2010; see their Figure 6e, bottom panel).
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Highlights
Several columnar structures are revealed in human V1 using high-resolution BOLD fMRI
Motion, color and disparity selective structures are found in human MT, and V2 and V3
High-resolution fMRI can help bridge human imaging and animal physiological studies
High-resoution fMRI helps understand how cortical microvasculature may be organized
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