Brain-heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field.

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Review Cite this article: Chang C, Raven EP, Duyn JH. 2016 Brain–heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field. Phil. Trans. R. Soc. A 374: 20150188. http://dx.doi.org/10.1098/rsta.2015.0188 Accepted: 5 February 2016 One contribution of 16 to a theme issue ‘Uncovering brain–heart information through advanced signal and image processing’. Subject Areas: biomedical engineering Keywords: functional magnetic resonance imaging, high-field imaging, high resolution, brain–heart Author for correspondence: Catie Chang e-mail: [email protected]

Brain–heart interactions: challenges and opportunities with functional magnetic resonance imaging at ultra-high field Catie Chang1 , Erika P. Raven1,2 and Jeff H. Duyn1 1 Advanced Magnetic Resonance Imaging Section, Laboratory of

Functional and Molecular Imaging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA 2 Center for Functional and Molecular Imaging, Georgetown University Medical Center, Washington, DC 20007, USA CC, 0000-0003-1541-9579 Magnetic resonance imaging (MRI) at ultra-high field (UHF) strengths (7 T and above) offers unique opportunities for studying the human brain with increased spatial resolution, contrast and sensitivity. However, its reliability can be compromised by factors such as head motion, image distortion and non-neural fluctuations of the functional MRI signal. The objective of this review is to provide a critical discussion of the advantages and tradeoffs associated with UHF imaging, focusing on the application to studying brain–heart interactions. We describe how UHF MRI may provide contrast and resolution benefits for measuring neural activity of regions involved in the control and mediation of autonomic processes, and in delineating such regions based on anatomical MRI contrast. Limitations arising from confounding signals are discussed, including challenges with distinguishing non-neural physiological effects from the neural signals of interest that reflect cardiorespiratory function. We also consider how recently developed data analysis techniques may be applied to high-field imaging data to uncover novel information about brain–heart interactions.

2016 The Author(s) Published by the Royal Society. All rights reserved.

1. Introduction

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rsta.royalsocietypublishing.org Phil. Trans. R. Soc. A 374: 20150188

Magnetic resonance imaging (MRI) has become the primary technique for investigating the macroscopic, systems-level structure and function of the living human brain. As the sensitivity of MRI to detecting neural activity improves with increasing field strength [1], there has been a continual movement towards higher-field imaging. Developments in ‘ultra-high-field’ MRI (UHF, referring to static magnetic fields of 7 T and above) have provided substantial gains in the spatial resolution and localization of neural activity, facilitating investigation of fine-scale cortical structure at the level of columns and layers, and of substructure in subcortical grey matter regions such as the basal ganglia and the thalamus (e.g. [2–4]). Throughout the past decade, high-field MRI has paved the way for novel discoveries and applications in multiple domains of human neuroscience (reviewed in [5–9]). Interactions between the heart and the brain are vital for maintaining homeostasis and survival in an ever-changing environment. Moreover, abnormalities in cardiovascular function and other aspects of the autonomic nervous system are reciprocally linked with numerous brain disorders (e.g. [10–14]; see also [15]). While current knowledge about the pathways underpinning brain–heart interactions is based primarily on studies in the anaesthetized animal [16,17], much is unknown about its circuitry in the human. The interaction between cardiac activity and attentional, cognitive and emotional processes (e.g. [18–20]) is also rich ground for discovery, and there is growing recognition of the concept that the brain may best be studied in the context of its unification with the rest of the body (e.g. [20,21]; also [22]). Methodologically, the scope of research on brain–heart interactions in the human has to date been limited by the difficulty of non-invasively localizing and measuring the neural activity of key subcortical structures, and in modelling their temporal interactions with the rest of the brain. A number of key nuclei of the brainstem and forebrain cannot be adequately resolved with positron emission tomography (PET) or conventional functional magnetic resonance imaging (fMRI) protocols because of their limited spatial resolution [23]. However, recent advances in neuroimaging technology and data analysis are showing promise in surmounting these barriers. In particular, UHF MRI appears well suited to applications in autonomic neuroscience, including the investigation of brain–heart interactions. The improvement in spatial localization at UHF may better enable the study of small structures involved in the control and mediation of cardiac activity, along with their interactions within distributed brain networks that facilitate adaptive modulation to environmental and internal demands. While limitations of fMRI at presently ‘standard’ field strengths of 3 T and 1.5 T effectively restrict spatial resolution to above 2 × 2 × 2 mm3 (with voxel dimensions of 2.5–4 mm being typical of whole-brain acquisitions), field strengths of 7 T and above—in conjunction with progress in hardware and accelerated imaging methods—are permitting voxel sizes of 1 mm3 and below. Accordingly, as discussed below, data acquired at high field allows for differentiating between sub-regions of brainstem, amygdala, thalamus and other substrates of autonomic function. UHF MRI also has substantial advantages for the study of brain anatomy: large increases in contrast with susceptibility-weighted imaging at UHF have allowed structural resolution to approach 200 µm [4] which, in combination with fMRI, can help to better characterize the regions involved in brain–heart interactions. Yet, the increasing severity of spatial and temporal artefacts with increased field strengths (see below) necessitates careful treatment as well as continued methodological development in both data acquisition and post-processing aspects. This article offers a review and perspective regarding the potential opportunities of highfield MRI (focusing on fMRI) in the study of brain–heart interactions, and outlines several major accompanying technological and physiological limitations. We also discuss recent and future directions for which high-field imaging data can be used in combination with novel data analysis techniques for probing reciprocal relationships between neural dynamics and cardiac activity. As our primary aim is to motivate the role of high-field imaging in studying brain–heart interactions, we refer the reader to review articles such as [5,6,24] for further information about high-field MRI physics and image acquisition.

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2. Functional magnetic resonance imaging at ultra-high field

Increases in CNR can be harnessed to image at higher spatial resolution, which is one of the main advantages of high-field MRI. The exact dependence of CNR on field strength (and on resolution) is complicated. For example, when neural activity produces BOLD contrast over a spatial extent greater than the voxel size, reducing the volume of the voxel will lead to a proportional decrease in contrast; however, when the spatial extent of contrast is smaller than the voxel size, reducing the voxel volume may not lead to appreciable reduction in contrast. In addition, while thermal noise is largely independent of voxel size, noise from physiological processes, subject motion and instrumental instabilities scale up with the signal level, and therefore will decrease when the voxel size is reduced (again, provided that the spatial extent of the noise source exceeds the voxel size) [32]. Thus, it is recommended that one decrease the voxel size until thermal noise is the dominant noise source. In practice, for voxel sizes of 1 mm3 and below, thermal noise will be the dominant noise source and the spatial extent of the functional contrast will often exceed the voxel size. Further reductions in voxel volume will lead to proportional (rapid) reductions in CNR. In summary, the increased contrast at high field allows one to reduce the voxel size. At a given voxel size, the benefits of high field are limited by non-thermal noise that is due to sources including physiological processes and head motion. In addition to reducing the voxel size such that thermal noise dominates, isolating and modelling structured physiological noise may further improve the CNR and is an active area of research (see §4).

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(a) Spatial resolution


fMRI [25–27] is a method for non-invasively measuring changes in brain activity over time (see [28] for a recent review). A time series of images is acquired wherein the fluctuation in intensity at a voxel over time indirectly reflects the dynamics of the local neural activity. The vast majority of fMRI studies are based on the blood-oxygen-level-dependent (BOLD) effect [29], by which changes in neural activity are coupled with local changes in blood flow and blood oxygenation. As the magnetic properties of oxygenated versus deoxygenated blood differ, this haemodynamic response to neural activity is detectable by MRI. The spatial and temporal scales (millimetres and seconds, respectively) of BOLD fMRI are too coarse to fully capture the breadth of information contained in neuronal activity; rather, it is limited to detecting slow changes in activity of large neuronal assemblies, and hence sophisticated experimental design and data analysis are required to optimally study brain function with fMRI. The functional involvement of a brain region can be examined by contrasting its time course of relative signal intensity in response to two or more well-controlled experimental conditions. More recently, spontaneous fluctuations in the fMRI signal, i.e. those that occur in the absence of (or seemingly independently of) explicitly presented stimuli and behaviour, have also been widely investigated and found to reveal topographies of distributed neurocognitive networks [30]. In both cases, the sensitivity of fMRI to detecting changes in neural activity depends on the ratio of contrast to noise (contrast-to-noise ratio, CNR). ‘Contrast’ reflects the magnitude of the fMRI signal change caused by neural activity, and ‘noise’ refers to temporal signal changes caused by other processes, often called ‘temporal noise’. Temporal noise contains contributions from the intrinsic MRI noise in each image (also called ‘thermal noise’) as well as structured fluctuations that are not related to neural activity, including head motion, instrumental instabilities and physiological (cardiac and respiratory) processes. In general, both BOLD contrast and physiological noise increase with the square of the field strength, while thermal noise increases linearly with field strength. Hence, CNR increases with field strength, though the gains are limited under conditions where there is a strong contribution from physiological noise [31]. The spatial specificity of BOLD contrast to neural activity also improves with increasing field strength, as the contribution from neural tissue increases at a faster rate than that of large vessels [1].

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(b) Spatial specificity and BOLD contrast mechanism

A potential drawback of UHF stems from magnetic field inhomogeneities caused by tissue interfaces with air and bone. Such inhomogeneities increase linearly with field strength and may cause image distortion as well as signal loss due to intra-voxel dephasing, also referred to as ‘susceptibility artefacts’. Improvements in accelerated imaging techniques and distortion-correction methods have been (and will continue to be) critical for minimizing these artefacts [5,7,37]. In typical fMRI studies, shimming cannot fully compensate for localized field inhomogeneities from susceptibility differences; this might only be possible for very small fields of view. Spin-echo methods, which suffer less from susceptibility-related signal loss compared to gradient-echo methods, benefit markedly at 7 T over 3 T due to the aforementioned changes in the BOLD contrast mechanism as well as from the increased CNR [35]. However, despite its gains at UHF, spin-echo techniques have reduced sensitivity and also result in greater RF power deposition and resulting tissue heating, limiting the extent of volume coverage ([38] and see [39] for a review).

(d) Applications of high-resolution functional magnetic resonance imaging at ultra-high field Capitalizing on recent advances in UHF imaging, a number of studies have demonstrated the ability to map features of neural organization with exquisite spatial detail. fMRI studies with in-plane resolutions of 1 mm and below have revealed columnar structure [2,40] and layerdependent specialization in cortex [3,41–43], tonotopic organization of the inferior colliculus [44] and benefits for characterizing networks at the whole-brain level [45].

3. Opportunities for ultra-high field functional magnetic resonance imaging in studying brain–heart interactions Knowledge about the neurocircuitry underlying brain–heart interactions has primarily been established through invasive studies in anaesthetized animals [17,46,47]. More recently,

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(c) Susceptibility artefacts and distortion


Importantly, imaging at higher spatial resolution does not necessarily improve the spatial accuracy of measuring neural activity. In addition to head movement and within-brain pulsatility, which are of greater concern as one moves to higher resolution, BOLD contrast arises from vascular sources that may be somewhat distant from the active neural tissue. This is particularly the case for the larger (pial) veins, which may contain blood draining from an active site several millimetres away [33]. In this regard, a notable advantage of UHF fMRI is the inherent increase in the signal contribution of small vessels/capillaries close to the site of neural activity. Briefly, increasing the field strength changes the degree to which different vascular compartments contribute to BOLD contrast: the intravascular (blood) contribution decreases (due to the substantial reduction in the T2 * of blood), but the extravascular contribution increases linearly near large vessels and quadratically near capillaries [34]. While the extravascular contribution near large vessels can still compromise spatial specificity, it is possible to suppress its contribution by performing a spin-echo acquisition (though at the expense of sensitivity and increased power deposition) [35] or by identifying them based on their appearance and location [36]. We note that, while BOLD fMRI contrast benefits substantially from UHF, this is not the case for all MRI contrasts. For example, the gains are much decreased with diffusion-weighted contrast due to the accelerated T2 relaxation at high field.

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The brainstem houses small and interconnected nuclei which, via ascending and descending projections to cortex and spinal cord, play a vital role in maintaining homeostatic control and adaptively modulating physiological responses to environmental demands. The brainstem presents substantial challenges for neuroimaging and, as such, has remained one of the leaststudied structures in the human brain in vivo. For instance, the brainstem is prone to high levels of cardiac and cerebrospinal fluid-induced pulsatile motion due to its proximity to large vessels and ventricles [51,52], and may also undergo bulk motion with the cardiac cycle [53,54]. In addition, prominent susceptibility artefacts may result from the bone and air-filled cavities such as the pontine cistern. Consequently, the BOLD signal quality in the brainstem is considerably lower than that of most other brain regions [55,56]. See [57] for a recent, detailed discussion focused on fMRI of the midbrain. Nonetheless, with attention to acquisition and post-processing strategies (reviewed in [56] and see §4), several groups have reported success in resolving fMRI signals from functionally distinct brainstem areas. The central circuitry underlying the baroreflex, i.e. the homeostatic regulation of blood pressure, was examined at 3 T with a concurrently recorded index of sympathetic output [58,59]. Using an interleaved acquisition scheme and optimized coverage of brainstem areas, increases in muscle sympathetic activity were associated with fMRI signal increases in the rostral ventrolateral medulla along with decreases in its caudal aspect and in the nucleus of the solitary tract, consistent with known baroreflex pathways (figure 2). At 7 T, the internal organization of the periaqueductal gray (PAG) rostrocaudally and into columns was shown to be functionally differentiated in emotional and volitional respiration tasks, using spatial resolutions of 0.75 mm and 1 mm, respectively [60,61]. In addition to studying the activation of individual brainstem nuclei, exploring their functional connectivity may also inform us about the mechanisms involved in brain–heart interactions. However, brainstem functional connectivity in the human is largely uncharted territory, and this endeavour may also be advanced as the possible gains of UHF fMRI are realized. Recently, Beissner et al. [23] demonstrated patterns of cortical connectivity with distinct brainstem subregions in human resting-state fMRI at 3 T, which was achieved by masking areas containing high physiological noise prior to assessing functional connectivity (a technique also suggested in [62]). A subsequent analysis of 7 T data yielded robust detection of brainstem nuclei in single subjects, whereas this detection required multi-subject averaging at 3 T [63]. Functional connectivity of the ventral tegmental area and substantia nigra, structures in the midbrain, displayed resting-state functional connectivity patterns within the brainstem (figure 3) and to various cortical regions at 7 T [55].

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(a) Functional magnetic resonance imaging of the brainstem

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non-invasive imaging techniques such as PET and fMRI have enabled further insight into central autonomic processing in the human, and in the context of higher-order emotional and cognitive responses [18,48,49]. The improved functional specificity of fMRI at higher field strengths is opening further opportunities for understanding the substrates and pathways of brain–heart interactions; many of the structures implicated in cardiovascular control and other autonomic functions consist of small-volume nuclei in the brainstem, as well as sub-regions of subcortical structures including hypothalamus, thalamus and amygdala, that cannot be adequately resolved at conventional field strengths and could benefit greatly from the increased resolution available at higher field (figure 1). Yet, critically, these regions are prone to susceptibility artefacts and physiological noise due to their locations near air–tissue boundaries and large vessels, limiting potential gains in functional contrast and requiring careful attention to acquisition and postprocessing techniques. Below, we discuss findings from several domains of neuroscience that exemplify the promise of high-field fMRI in studying brain regions reported to be common to, or of similar size to and imaging difficulty as, structures likely implicated in human brain–heart interactions.

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anterior cingulate cortex

forebrain

insular cortex

thalamus hypothalamus

amygdaloid nuclear complex 30 mm baroreceptor afferents

medulla

nucleus of the solitary tract ventrolateral medulla

nucleus ambiguus

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intermediolateral cell column

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Figure 1. Several key areas involved in brain–heart interactions. Selected areas of the forebrain, brainstem and spinal cord involved in human autonomic function are depicted. Arrows illustrate major pathways of the baroreceptor reflex, mediating the homeostatic control of blood pressure (left and right branches stemming from the baroreceptor afferents indicate parasympathetic and sympathetic outflow, respectively). Scale bars provide approximate dimensions in the human brain. For reviews, see [17,18,46,47]. Figure design inspired by Benarroch et al. [50]. (Online version in colour.)

(b) Functional magnetic resonance imaging of forebrain regions Autonomic pathways of the brainstem and spinal cord are integrated with, and mediated by, higher centres in cortex as well as subcortical and limbic regions including hypothalamus,

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anterior cingulate cortex


medial prefrontal cortex

axial

posterior

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rostral

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RVLM

anterior

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Figure 2. fMRI signal intensity changes correlated with sympathetic outflow. fMRI signal intensity changes correlated with spontaneous fluctuations in muscle sympathetic nerve activity (MSNA) in eight subjects. Increases (hot colour scale) and decreases (cool colour scale) in fMRI signal intensity with increases in MSNA are overlaid onto a series of axial fMRI slices from an individual subject. RVLM, rostral ventrolateral medulla; NTS, nucleus tractus solitarius; CVLM, caudal ventrolateral medulla; SI, signal intensity. Adapted from [58]. (Online version in colour.) T-stat

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VTA 8 6

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Figure 3. Functional connectivity in the midbrain at 7 T. Functional connectivity maps at 7 T in a representative participant, obtained with a three-dimensional fast field echo (FFE) sequence. An uncorrected voxel level threshold of p < 10−6 (cluster size = 30 voxels) was used. Seed regions of interest in the midbrain include left substantia nigra (L_SN), right substantia nigra (R_SN), left ventral tegmental area (L_VTA) and right ventral tegmental area (R_VTA). Colour bar represents T-statistic. FFE voxel size = 1.33 mm3 . Axial midbrain slice sections are displayed at the level of the superior colliculus for N = 6 participants. L denotes the left side of the brain and A denotes the anterior portion of the brain. Data underwent motion correction, RETROICOR, band-pass filtering between 0.01 and 0.1 Hz. Robust bilateral spatial distribution of functional connectivity was observed in most ROIs. Adapted from [55]. (Online version in colour.)

Cortical areas, prominently the insular, cingulate and prefrontal cortices, also have a wellestablished involvement in cardiovascular modulation (reviewed in [18,71,72]). Non-invasive imaging is making exciting headway in extending findings from the animal to the human and in shedding light on the integration between the autonomic nervous system, cognition and emotion. At present, an array of findings point to the complexity of cortical autonomic processing, suggesting that different pathways may be recruited depending on the particular task employed (e.g. affective, cognitive, motor [73]) and also perhaps as a function of individual experience [48]. Meta-analyses are beginning to elucidate the segregation and integration between parasympathetic and sympathetic divisions [73,74] and to interrogate their relationship with large-scale networks such as default-mode and salience networks (discussed in [18,73]; see also [19] with regard to attentional and affective networks). Of particular relevance to UHF fMRI, regions attributed to parasympathetic and sympathetic involvement described in [73] were situated in closely overlapping clusters of anterior insula, angular gyrus and amygdala that may be more precisely delineated with high spatial resolution. fMRI at UHF may convey unique benefits in studying interactions between cortical and subcortical areas. For instance, the thalamus—containing nuclei through which information related to brain–heart interactions is relayed between the cortex and subcortical/spinal cord

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(c) Studying large-scale interactions with high-resolution functional magnetic resonance imaging

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thalamus and amygdala. These structures adaptively modulate cardiovascular activity in response to changing emotional, cognitive and physical demands [50]. Like the brainstem, these regions have characteristics that present formidable challenges for neuroimaging. For one, they are small and themselves contain heterogeneous subdivisions, though the majority of fMRI studies effectively treat them as uniform areas, as it is difficult to resolve their finer structure. This may give rise to apparent variability across the literature in their reported functions and may vastly oversimplify any resulting inferences. Further, ventral brain areas (e.g. amygdala, hypothalamus, nucleus accumbens) suffer from susceptibility artefacts as well as elevated amounts of physiological fluctuation. Advances in UHF fMRI are showing promise in obtaining sensitive measures of neural activity from forebrain areas and in revealing their fine internal organization. Considerable improvement in the BOLD CNR of the amygdala was observed at 7 T compared to 3 T in an emotion discrimination task [64], and the use of smaller voxels has been shown to mitigate susceptibility-induced signal dropout in the amygdala and orbitofrontal cortex [65]. Interestingly, high-resolution imaging at 3 T has also indicated the possible involvement of the amygdala in the default-mode network, an observation not typically made at lower resolutions [66], and has revealed valence-dependent responses to emotional stimuli in the hypothalamus, suggesting a broader role for the latter in emotional processing than is typically recognized [67]. Data-driven clustering methods at conventional image resolutions (3 mm isotropic) at 3 T have suggested that three primary amygdalar subdivisions can be identified on the basis of distinct resting-state connectivity patterns [68], and there is evidence that the performance of connectivity-based parcellation techniques will improve with even higher field [63]. Very recently, resting-state functional connectivity of the bed nucleus of the stria terminalis, a portion of the ‘extended amygdala’ that is implicated in autonomic arousal and anxiety, was mapped in humans at 7 T at 1.3 mm isotropic resolution [69]. In contrast with a similar analysis conducted at 3 T [70], greater specificity was reported in identifying functional connectivity to small structures (e.g. sublenticular extended amygdala, PAG, hypothalamus and habenula) and subregions of hippocampus and thalamus. Collectively, these studies suggest that improvements in resolving and measuring neural activity in ventral brain areas may enable novel studies of their involvement in emotional and stress-induced cardiac responses, as well as their integration with other cortical and subcortical regions.

UHF MRI is a burgeoning component of large multi-site neuroimaging projects, including the Human Connectome Project ([80], which includes a 7 T protocol). Progress towards optimizing imaging hardware, acquisition and data analysis in association with these projects [81] is likely to continue advancing the use of UHF and its role in understanding brain–heart interactions. The growing number of open data repositories are also broadening the accessibility of UHF data ([82,83] and see [5] for further discussion of practical issues concerning UHF scanners). While UHF is not presently established in clinical practice, its advantages over lower field strengths for visualizing certain pathological features offers clear promise for the diagnosis and understanding of disease processes [5,84,85]. As one possible future direction for fMRI, the activity of deep-brain and brainstem nuclei—along with their functional interactions with the rest of the brain—may be contrasted between normal and pathological conditions for insight into how human brainstem abnormalities may underlie autonomic impairments. Preliminary research at 3 T provides evidence that in major depression, a condition marked by reduced heart rate (HR) variability [86,87], autonomic dysregulation may be linked with deficient functional connectivity between autonomic brainstem nuclei and the rostral anterior cingulate [88].

4. Challenges in ultra-high-field imaging of brain–heart interactions While the enhanced temporal contrast and spatial fidelity of UHF MRI make it an appealing tool for the study of brain–heart interactions, there are a number of counteracting technical and analytic hurdles. Several issues pertaining to the study of brain–heart interactions are discussed below.

(a) Balancing regional sensitivity with wide spatial coverage One challenge in studying large-scale functional interactions with small nuclei involves balancing optimized acquisition of specific brain areas with the desire for broad spatial coverage. Owing to limitations in MRI acquisition speed, high-resolution fMRI of specific brain regions has typically been achieved by restricting the field of view such that it covers only a fraction of the brain. Fortunately, continued progress in hardware and accelerated imaging techniques is enabling the acquisition of high-resolution images with extensive spatial coverage [89,90]. Leveraging advances in gradient and RF hardware along with parallel imaging, De Martino et al. [45] examined resting-state networks as a function of resolution (1–2 mm isotropic) in 7 T data with whole-brain coverage [45]. Beyond demonstrating the detection of canonical restingstate networks at high resolution (with 1.5 mm reported to be the best compromise in their comparisons), higher spatial resolutions were found to yield significantly reduced partial volume effects and display tighter correspondence with the subject-specific anatomic images. However, default-mode network connectivity in frontal brain areas (ACC) was reduced compared to that seen in lower-field data acquired at lower resolution, an effect suggested to result from reduced

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(d) Outlook and clinical potential

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regions [14,17]—has a well-described functional parcellation in animals that is also supported by non-invasive human studies (e.g. [75,76]), though its nuclei are not commonly differentiated in human fMRI reports. Functional integration of different thalamic sub-regions with distinct cortical and basal ganglia circuits may be studied with greater precision with UHF fMRI [77]. In one study at 7 T, distinct activation of mediodorsal versus centromedian/parafascicular thalamic nuclei was observed in emotional versus anticipatory attention phases of a task; further, these nuclei exhibited distinct co-activation with affective and cognitive divisions, respectively, of the anterior cingulate cortex (ACC) and insula [78]. Although much of the neocortex can be imaged with adequate sensitivity at 3 T and below, examination of cortical subdivisions having differential autonomic involvement, e.g. within the insula [79], may also be better accomplished with UHF fMRI.

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7T

RN Mi

P

Me

Figure 4. High-resolution anatomical contrast at 7 T. This sagittal image was acquired at 7 T using a two-dimensional gradient-echo sequence and elongated head coil for full brainstem coverage. Spatial resolution is 0.25 mm in-plane (slice thickness = 2 mm), allowing for detailed discrimination of brainstem structures and nuclei. Th, thalamus; AC, anterior commissure; Mi, midbrain; RN, red nucleus; P, pons; Me, medulla. Adapted from [92]. SNR in the high-resolution data. Indeed, optimal spatial resolutions may depend on the brain region and particular neuroscience question of interest [6]. Also, consistent with observations in the visual cortex by Bianciardi et al. [91], BOLD signals of opposite temporal phase near large draining vessels were identified in the precuneus, suggesting that large-vessel effects are still present at high field and may interfere with interpretation of resting-state fMRI.

(b) Combining anatomy and function Localizing activation observed with BOLD contrast fMRI, particularly within zones of small, densely situated nuclei, can be exceedingly difficult and requires precise alignment with an anatomical reference image. Fortunately, structural MRI also benefits from increased resolution and contrast available at UHF (figure 4). Susceptibility-weighted imaging at field strengths of 7 T and above has improved the visualization of structures, including white matter fibres and cortical layers, of the order of hundreds of micrometres [5]. Anatomic parcellation of amygdalar and hypothalamic subdivisons, and delineation of midbrain dopaminergic structures, has also seen improvement at 7 T compared to lower field strengths [93–96]. Efforts to evaluate different anatomic contrasts (and combinations thereof) for in vivo visualization of brainstem nuclei are under way [97,98], as are assessments of optimal processing and alignment strategies for both functional and structural data [99–101]. Recent studies have taken initial steps towards mapping human brainstem nuclei into a standard-space atlas (at 7 T [97]) and towards visualizing neuroanatomic connections between autonomic nuclei in the brainstem and limbic forebrain sites using high-resolution diffusion spectrum imaging tractography (at 3 T [102]).

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AC


Th

Non-neural sources of temporal fluctuation have been estimated to constitute more than half of the total fMRI signal variability at 7 T [108]; also, with BOLD contrast comprising only a few per cent of the raw signal, effect sizes in cognitive neuroscience studies tend to be small, and accounting for non-neural influences can critically influence the outcome of analysis. Below, we discuss three major classes of temporal noise.

(i) Physiological noise: non-BOLD Physiological noise refers to temporal noise that arises from systemic physiology, primarily from cardiac and respiratory processes (see [56,109] for reviews). Physiological noise is amplified at high magnetic fields and influences fMRI time series through multiple distinct mechanisms. One class of physiological noise results in non-BOLD fMRI signal deflections that are synchronized with the respiratory and cardiac cycles [51,110,111]. Static magnetic field modulations caused by the motion of the chest during breathing will result in voxel shifts or blurring, causing the greatest deflections at boundaries between tissue compartments and around the edges of the brain [112]; the cardiac cycle can induce cerebral pulsation and inflow (T1 ) effects [51]. As noted above, these artefacts unfortunately affect the brainstem and other regions that reside near vessels and fluid-filled cavities. Retrospective filtering methods [113,114], including RETROICOR [115] and independent component analysis (ICA [116,117]) have shown success in reducing these cyclic, non-BOLD artefacts, even in the brainstem [118,119]. On the acquisition side, artefacts from respiratory motion can be additionally reduced by the use of navigator methods to demodulate dynamic phase shifts [120,121]. Cardiac gating (synchronizing the fMRI image acquisition with the cardiac cycle) is often used to minimize pulsation artefacts, and undesired fluctuations introduced by the (heart-dependent) variability in TR can be normalized by collecting images at multiple echo times per TR (‘multi-echo imaging’) [100,122].

(ii) Physiological noise: BOLD A second form of physiological noise acts directly on BOLD contrast by dynamically altering cerebral blood flow and volume. In other words, BOLD signal changes that are not coupled to the local neural activity (and thereby regarded as ‘noise’) can be induced through systemic respiratory and cardiac modulation of variables such as arterial CO2 and blood pressure. An extreme example is breath holding, which is known to trigger a spatially extensive, large-amplitude BOLD signal change [123] through physiological mechanisms postulated in, for example, [124,125]. The more subtle, inevitable changes in respiration volume and rate (RV [126,127]) (as well as HR [128,129]) across a typical fMRI are also found to induce considerable fluctuation in widespread, grey matter BOLD signal, compromising the ability to detect resting-state networks. These haemodynamic RV and HR effects are, compared to the non-BOLD artefacts described above, more difficult to disentangle from neurally driven BOLD due to their shared spatial and temporal characteristics;

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(c) Temporal noise

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Importantly, the increased severity of distortions at UHF poses a major challenge for aligning functional and anatomic data. Tools for distortion correction are available and under development (e.g. [103,104]). Acquiring T1 -contrast echo-planar images with matched distortion can also facilitate registration to anatomical landmarks or serve as an intermediate step between aligning distorted functional images with a non-distorted T1 -contrast anatomic reference [105]. The quality of T1 -weighted images may also be compromised at high field, due to the increased propensity for B1 field inhomogeneities, though recent improvements in RF coil technology and pulse sequences (e.g. [106]) can greatly ameliorate these issues. Finally, it is possible that standard tools for wholebrain spatial registration may not perform well at high (less than 1 mm) spatial resolution; beyond the lower tolerance of high-resolution data to head motion, true differences in fine-scale functional localization across individuals introduce challenges in assessing results at the group level [107].

Examples of non-physiological noise sources include head motion and instrumental drift. Some of these noise sources are very slow and may be suppressed by filtering out very low frequencies or regressing out low-order polynomials [148]; however, the challenge is to remove such nuisance fluctuations without removing BOLD signal of interest. One alternative is to use regressors

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(iii) Non-physiological sources of noise

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linear transfer function models have been found to provide effective nuisance regressors [129–131], though further investigation is needed. The mechanism by which HR correlates with BOLD is not as well understood as that of respiratory modulation. One possibility is that correlations between HR and the BOLD signal arise indirectly from correlations between respiration and HR (e.g. respiratory sinus arrhythmia [132]); it is also plausible that HR acts independently from respiration to some degree, separately modulating blood pressure and haemodynamics. While correlations between HR and BOLD are observed to be quite global in nature [128,129], suggestive of systemic modulation rather than particular neural substrates of cardiac regulation, it is difficult to rule out (or disentangle) the contribution of neural activity. Another possibility is that changes in vigilance and arousal state, which are associated with changes in both HR and global neural activity [133,134], mediate the observed correlation between HR and BOLD. Indeed, physiological noise (particularly that of the BOLD variety) poses an obvious confound for studies investigating neural correlates of autonomic processing. Such studies have employed tasks that actively modulate physiology (e.g. Valsalva manoeuvre [135], volitional breath holding [136] and hand-grip effort [137,138]), and/or they assess relationships between fMRI signals and simultaneously acquired peripheral autonomic measures (e.g. HR variability (HRV, [74,139]), skin conductance [140,141] and microneurographic recordings of nerve activity [59]). Respiratory manoeuvres and physical tasks can elicit large changes in systemic physiology and subject motion that can obscure specific neural responses. Passive monitoring of autonomic variables may reduce such artefacts, but also elicits less autonomic variation and still does not circumvent the question of disambiguating autonomic neural responses from physiological noise (and from activity underlying cognitive/mental effort or skeletal motor activity in performing a task). How can one disentangle non-neural physiological influences on fMRI signals from those implicated in cardio-respiratory control [142]? One possibility for dissociating BOLD ‘noise’ from neural signals arises when these components possess spatial and/or temporal separability. Birn et al. [143] suggested that the temporal dynamics of the artefactual, respiratory-driven BOLD signal modulation may differ enough from the haemodynamic response to neural activity such that they can be disentangled to some extent using linear modelling [143]. The transfer function between RV and BOLD signal time series indeed exhibits longer latency (and primarily of opposite polarity) compared to the haemodynamic response of BOLD to neural activity [129,144]. Dissociation between vascular and neural signals in respiratory paradigms may also be augmented by experimentally manipulating the time course of the former. As two examples: (i) a cued-breathing task was performed in addition to a respiratory challenge at 7 T in an effort to discern brainstem areas related to volitional control of breathing [60]; and (ii) BOLD signal changes from external CO2 administration were contrasted with those correlated with natural fluctuations in end-tidal CO2 , with the hypothesis that the coupling between PaCO2 and BOLD would become stronger in respiratory control centres [145]. Spatial ICA appears to capture well the cyclic cardiac pulsation and respiratory artefact into a collection of ‘noise’ components, though is less effective for BOLD respiratory volume/rate artefacts [146], perhaps due to the prevalence of the latter in grey matter and its strong spatial overlap with task- and resting-state networks. ICA in conjunction with a multi-echo pulse sequence can offer a principled way of sorting BOLD and non-BOLD components based on their echo-time dependence (see below), though again demonstrating better performance in dissociating cyclic, non-BOLD physiological noise [118,147] compared to BOLD physiological noise.

Recent developments in high-dimensional data analysis may, in conjunction with UHF fMRI, uncover novel information about brain–heart interactions and their deviation in pathological states. We briefly discuss two relevant directions.

(a) Dynamic measures of functional interaction Methods for studying the interactions between brain regions typically assume that temporal covariation is fixed over time, particularly in resting-state fMRI. While static analysis frameworks have yielded tremendous insight into large-scale functional architecture, considerable variation over time can be observed in the properties of resting-state fMRI time series from individual brain regions [157,158], and in metrics of inter-regional coupling [159,160]. Variability in intrinsic activity has been suggested to arise in part from changes in arousal and autonomic processes, underscoring the state dependence of spontaneous brain activity and connectivity [140,161–163]. A better understanding of autonomic influences on intrinsic fluctuations may shed light on previously unexplained patterns of structured brain activity, as well as provide a new window into the neurobiology of brain–body interactions. In addition to mapping regions whose fMRI activity tracks fluctuations in peripheral autonomic indices such as HRV, one may also ask whether time-varying autonomic states are linked with time-varying associations between brain regions. In an exploratory study of this nature [162], spontaneous fluctuations in HRV—whose power in high frequencies (0.15–0.4 Hz) is regarded as an indirect measure of parasympathetic activity—were accompanied by changes in functional connectivity with regard to the dorsal anterior cingulate cortex (dACC) and amygdala. A cluster in the brainstem whose correlation with dACC and amygdala specifically tracked highfrequency HRV was identified; while the low spatial resolution of the data precluded localization to specific nuclei, the analysis framework could certainly be extended to resolve autonomic nuclei in the brainstem and elsewhere with higher spatial resolution. The extent to which time-averaged functional connectivity differences between clinical populations are mediated by varying autonomic states (and corresponding functional connectivity differences) in neurological and psychiatric disorders is also an open question. A number of approaches for analysing time-varying functional interactions have been proposed. Recently, a multivariate technique was described by Liu et al. wherein each BOLD fMRI image (volume) is regarded as a snapshot of co-activation, and representative co-activation patterns (CAPs) are then derived by clustering a set of volumes based on their spatial similarity [164]. CAPs derived from resting-state data revealed transient associations between brain regions that are not apparent from typical, longer time averages; the default-mode network, for instance, appears to co-activate with different areas and sub-networks within salience and executive control networks at different points in time. We may speculate that greater spatial detail revealed by analysing CAPs (and extensions thereof [165]) at higher resolution, and in conjunction with

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5. New directions in analysis of brain–heart interactions

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based on motion parameters or reference regions within the brain that are known to be neurally inactive [149,150]. Another approach is to examine the dependence on echo time: non-BOLD noise should scale with MRI signal strength and decay approximately exponentially with echo time, whereas BOLD signals show a characteristic gamma-variate dependence on echo time [151]. Thus, by performing a multi-echo acquisition, it may be possible to isolate non-BOLD noise sources [152,153]. Recent work has demonstrated that ICA of multi-echo data can offer a practical approach for mitigating non-BOLD sources of fluctuation [118,147,154,155]. It will be interesting to see whether this, and other filtering approaches, will enable the study of autonomic processes that vary too slowly to be resolved with conventional BOLD fMRI, such as tonically active sites of autonomic control. One drawback of the multi-echo approach is the high data acquisition rate, which complicates the acquisition of fMRI scans with high spatial and temporal resolution; the use of simultaneous multi-slice excitation may facilitate this (discussed in [156]).

cardiac measurements, may provide a new technique for investigating the network behaviour underlying brain–heart interactions.

6. Summary High-resolution fMRI at magnetic fields at and above 7 T has yielded remarkably fine-scale measurements of neural activity and structure, and is currently well poised for novel discoveries about interactions between the heart and the brain. The ability to achieve sub-millimetre functional and anatomic resolution in vivo permits the study of small nuclei in areas such as brainstem and forebrain that serve as key integration and relay nodes for bidirectional brain–heart modulation. Advances in accelerated imaging also permit studying their large-scale functional connectivity, which may be further facilitated by drawing upon recent concepts in spatiotemporal data analysis. However, successfully isolating functional signals from small regions, especially in areas strongly contaminated by physiological and susceptibility artefacts, requires considerable effort in acquisition and post-processing. Subject motion can obscure signals from smaller, densely interwoven nuclei (even if the imaging resolution can be further increased), and disentangling temporal noise from autonomic neural activity remains an outstanding challenge. Nonetheless, evidence suggests that UHF MRI will continue to open unique opportunities for investigating brain–heart interactions in the human. Competing interests. We declare we have no competing interests. Funding. This research was supported by the Intramural Research Program of the National Institutes of Health, National Institute of Neurological Disorders and Stroke. This material is based in part upon work supported by the National Science Foundation Graduate Research Fellowship under grant no. DGE-1444316.

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Machine learning methods are having a rapidly growing impact on the neuroimaging field (reviewed in [166,167]). Distinct from traditional univariate approaches, which query the activity of individual voxels, multivariate methods capitalize on the joint activity expressed over collections of voxels to understand differences in brain states, differences in structure or function between clinical populations, and neural representations of stimuli, mental imagery and dream content. A possible future direction may use multivariate pattern analysis in the study of brain– heart interactions. For instance, within small-volume regions of highly heterogeneous structure, the pattern of activity across a neighbourhood of voxels (i.e. ‘searchlight’ [168]) spanning functional subdivisions may reveal complementary information about how they jointly encode or underlie changes in autonomic state. We may speculate that UHF imaging and increased spatial resolution enhance the information that can be mapped at fine scales with multivariate methods (an idea also suggested and further described in [6,24]). The benefit of higher resolution may depend on the particular brain regions studied as well as dimensionality reduction trade-offs (e.g. using more, lower-resolution brain areas in a classifier compared to fewer high-resolution areas) and the particular classification methods applied. In addition, there are several examples in which discriminative information is shown to persist at coarse spatial scales, the underlying mechanisms of which are under discussion (e.g. [169–172]). Presently, whether high-resolution fMRI can offer advantages for multivariate pattern analysis in assessing brain–heart interactions has yet to be determined; the potential benefits may be countered by significant trade-offs (see [173] and those discussed above), and is a topic currently under investigation (e.g. [174]).


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