Stand up too fast and you know what happens next. You will feel faint as the blood rushes away from your head. Gravity pulls the blood into your legs, and your blood pressure drops. To correct this imbalance, the brain sends nerve impulses telling the heart to beat faster and the outer blood vessels to tighten. This is the autonomic nervous system at work. It is how the brain adjusts cardiac output, and quietly controls other internal organs in the body. It involves two key regions of the brain, the hypothalamus and the brainstem, and stimulates smooth muscles and glands around the body.

The cardiovascular system also responds to the demands of exercise, with the heart supplying fresh blood laden with oxygen and the blood clearing out waste materials as it flows around the body. Perhaps surprisingly, blood pressure and heart rate fluctuate even at rest. The heart beats faster when breathing in and slower when breathing out. People’s blood pressure, the force that keeps blood moving through arteries, also oscillates in so-called Mayer waves that last about 10 seconds.

Much of the current understanding of the inner workings of the cardiovascular system – and how it is regulated by the brain – stems from animal experiments. This is because few attempts have been made to simultaneously measure how a person’s brain and cardiovascular system work with enough detail to see how brain waves and cardiac oscillations might interact.

To achieve this, Manuel et al. have now measured the brain activity, pulse and blood pressure of twenty-two healthy people while they were lying down in an MRI machine. This revealed that three distinct parts of the hypothalamus regulate cardiovascular output in humans. These ‘subsystems’ communicate with each other and with the lower brainstem, which sits beneath the hypothalamus. Manuel et al. also observed that the rhythmic activity of these subsystems runs in sync with oscillations typically seen in heart rate and blood pressure.

With this work, Manuel et al. have shown that it is feasible to measure different systems of cardiovascular control in humans. In time, with further experiments using this new approach, the understanding of chronic high blood pressure and heart failure may improve.

Given the importance of cardiovascular regulation for our daily survival, it is not surprising that the body has several redundant and mutually interacting systems for this task. Important representatives include the classical baroreceptor (arterial and cardiopulmonary) and chemoreceptor reflexes, neuroendocrine systems, like the vasopressin, sympatho-adrenal, renin-angiotensin-aldosterone, and the more recently discovered leptin-melanocortin system (Sawchenko and Swanson, 1981; Dampney, 1994; Hilzendeger et al., 2012; Salman, 2016).

Much of our present understanding of these systems stems from animals experiments, while mechanistic studies in humans remain scarce, mostly for lack of non-invasive methods to assess subcortical brain activity. Furthermore, most studies have evaluated only one system at a time. We must assume, however, that all neural and neuroendocrine cardiovascular control systems are carefully orchestrated by the central nervous system to achieve optimal regulation. The control centres responsible for such orchestration are presumed to be located in the brainstem and hypothalamus. They are well characterised for the ‘textbook’ baroreflex, but much less for the other systems.

To study cardiovascular regulation in human subjects, we devised an MR-compatible lower body negative pressure (LBNP) chamber that simulates orthostatic stress via footward blood volume displacement (Figure 1c; Goswami et al., 2008). The pressure of −30 mmHg used in our experiment has been shown to recruit both neural and endocrine mechanisms of cardiovascular regulation via activation of cardiopulmonary and arterial baroreceptors (Loewy, 1981; Mark and Mancia, 1983; Kimmerly et al., 2005; Salman, 2016). While assessment of endocrine pathways requires invasive methods, neural cardiovascular regulation by the autonomic nervous system can be measured non-invasively as it leads to characteristic rhythms of heart rate and blood pressure variability (HRV, BPV). To assess these rhythmic changes, we recorded blood pressure and heart rate traces during the LBNP-fMRI measurements and subjected them to spectral analysis isolating two frequency bands: low frequency blood pressure variability (LF_BPV, ~0.1 Hz), reflecting the so-called Mayer waves of sympathetic origin (Julien, 2006); and high frequency heart rate variability (HF_HRV,~0.28 Hz) reflecting the primarily vagally mediated respiratory sinus arrhythmia (Billman, 2013). Note that both the LF and HF bands lie at the top or above the canonical frequency range of the BOLD signal (<0.1 Hz, Figure 1i); although several studies have recently provided evidence for BOLD oscillations beyond this frequency range (Chen and Glover, 2015; Lewis et al., 2016).

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Subcortical control regions of cardiovascular function are mainly located in the hypothalamus, comprising the paraventricular nucleus (PVN), lateral hypothalamic area (LH), arcuate nucleus (Arc), dorsomedial nucleus (DMH), and median preoptic nucleus (Supplementary file 1), and in the lower brainstem, comprising the nucleus of the solitary tract (NTS), rostral and caudal ventrolateral medulla (RVLM/CVLM), nucleus ambiguus (Amb), and the caudal raphe nuclei, that is nucleus raphe obscurus (ROb) and nucleus raphe pallidus (RPa) (Supplementary file 2; Coote, 2004; Dampney, 1994; Loewy, 1981; Saper et al., 2015; Benarroch, 1993). Since these areas are notoriously hard to investigate in humans due to their small size, depth within the skull, and physiological noise from surrounding vessels and cerebrospinal fluid (Brooks et al., 2013; Beissner, 2015), we devised a high-resolution functional MRI approach and preprocessing pipeline specifically optimised for subcortical imaging. In particular, we balanced spatial and temporal resolution (2×2×2 mm³, 1.23 s) to distinguish neighbouring nuclei, while critically sampling respiratory frequencies. Preprocessing involved maximising anatomical specificity by applying advanced distortion correction, symmetric diffeomorphic image registration to a study template, and omitting any spatial smoothing. In contrast to common practice, we did not regress physiological fluctuations from our data to avoid removing meaningful signal from the cardiovascular regions we were interested in Iacovella and Hasson, 2011. Instead, we opted for a spatial noise correction approach (Beissner et al., 2014) as part of our group-level statistical analysis.

LBNP led to a significant reduction of systolic blood pressure (Rest: 132.6 ± 20.2 mmHg, LBNP: 113.4 ± 20.2 mmHg, t(17)=-11.3, p<0.001, Cohen's d = −0.95) and a compensatory decrease of the interbeat interval (Rest: 0.95 ± 0.12 s, LBNP: 0.85 ± 0.11 s, t(20)=-4.9, p<0.001, d = −0.96), while measures of respiration (respiratory interval, respiratory volume, and respiratory volume per time) showed no significant changes. Furthermore, head movement was not significantly different between LBNP and Rest periods. We also observed increased spectral power of LF_BPV (Rest: 0.14 ± 0.03, LBNP: 0.17 ± 0.04, t(17)=2.9, p=0.011, d = 0.97) and reduced spectral power of HF_HRV (Rest: 0.18 ± 0.08, LBNP: 0.14 ± 0.08, t(20)=-2.4, p=0.0267, d = −0.47) indicating sympathetic excitation and vagal inhibition in response to the cardiovascular challenge, respectively (Figure 1d,e).

To identify cardiovascular control centres within the hypothalamus, we followed a two-step process; namely segmentation using masked independent component analysis (mICA) (Beissner et al., 2014) of the fMRI time series, followed by testing for cardiovascular involvement using two independent criteria. In the first step, the hypothalamus was segmented into 49 functionally distinct regions (Figure 1—figure supplement 1), followed by removal of six unspecific components (Figure 1f), leaving 43 components. The initial number 49 was derived by maximising mICA reproducibility, see Figure 1g. This high-dimensional segmentation yielded the characteristic three medio-lateral zones and three rostro-caudal regions expected from post-mortem anatomical studies (Dudás, 2013; Figure 1j, Figure 1—figure supplement 2, Supplementary file 3). The lower-dimensional decompositions with 8 and 10 subregions showed only two medio-lateral zones, and were thus not analysed. They were, however, consulted later to elucidate averaged intra-hypothalamic functional connectivity (see below). In the second step, each of the 43 (49 - 6) hypothalamic regions was tested for two criteria, namely LBNP-related changes in functional connectivity (Δfc_LBNP) with any region of the hypothalamus or lower brainstem, and changes in BOLD spectral power in one or both of the two cardiovascular frequency bands (Figure 1h).

Our analysis revealed five hypothalamic regions fulfilling both criteria for cardiovascular involvement (Figure 2, Supplementary File 4): the right anterior, and bilateral tuberal LH/supraoptic nucleus (SON, Figure 2a–c), the right tuberal PVN/posterior hypothalamic area (PH) (Figure 2d), and the arcuate nucleus (Figure 2e). Detailed information on the spatial distribution of BOLD spectral power in the two cardiovascular frequency bands is provided in Figure 2—figure supplement 1. All identified regions except the bilateral tuberal LH/SON showed positive Δfc_LBNP with the lower brainstem (Figure 2a,d–e). In contrast, Δfc_LBNP of the tuberal LH/SON was restricted to the hypothalamus. Here, both sides showed positive within-nucleus Δfc_LBNP, which can be interpreted as local activity changes, while only the left LH/SON showed additional negative Δfc_LBNP with the arcuate nucleus (Figure 2b–c).

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Further insights on hypothalamic functional connectivity of the identified regions came from the analysis of the low-dimensional ICA results (Figure 3). Here, inter-dimensional matching showed that right anterior LH/SON, PVN/PH, and Arc had low-dimensional equivalents (Figure 3b,d), while the bilateral tuberal LH/SON did not. The low-dimensional versions of Arc and right anterior LH both included portions of the PVN/PH and adjacent nuclei.

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Our supplementary analyses showed that our results are not driven by physiological noise. As the first analysis revealed, physiological noise regression had little effect on the masked ICA results as evidenced by the functional segmentation still showing the same known anatomical subdivisions of the hypothalamus (Figure 2—figure supplement 2). This was expected as we had previously shown that noise regression has little effect on masked ICA results (Beissner et al., 2014). Moreover, the ICs matching our five hypothalamic regions from the main analysis (spatial correlation r > 0.89) showed similar, yet smaller functional connectivity changes in response to cardiovascular challenge (Figure 2, Figure 2—figure supplement 3). The second supplementary analysis revealed that four of the five hypothalamic regions from the main analysis showed significant functional connectivity with cerebral regions that was clearly dominated by grey matter (Figure 2—figure supplement 4), while noise components would be expected to mainly show white matter or ventricular connectivity. The one remaining region was the PVN/PH, which showed very little cerebral connectivity at all.

We found five hypothalamic regions involved in cardiovascular regulation: the right anterior and bilateral tuberal LH/SON, the right tuberal PVN/PH and the arcuate nucleus. This selection of nuclei agrees with previous results from studies using Fos-like protein expression in response to prolonged hypotension (Li and Dampney, 1994; Figure 2—figure supplement 5).

Based on functional connectivity changes between the original hypothalamic regions and the lower brainstem (Figure 2), we were able to distinguish three major hypothalamic cardiovascular control systems (Figure 4c–e). The first was characterised by positive Δfc_LBNP of the PVN/PH with a region in the ipsilateral lateral medulla comprising the RVLM, inferior olivary (IO), parvicellular reticular nucleus (PCRt), Amb, and intermediate reticular (IRt) nuclei, as well as negative Δfc_LBNP of the PVN/PH with itself (Figure 2d). We suggest that this system represents the ‘textbook’ baroreflex arc with its sympathetic (RVLM) and vagal arms (Amb); both under hypothalamic control by the PVN. Since baroreflex regulation classically involves the NTS and caudal ventrolateral medulla (CVLM), we conducted a supplementary mICA in the lower brainstem. This analysis yielded a functionally distinct region matching the above-mentioned RVLM cluster which was tested for Δfc_LBNP. As expected, a single cluster in the ipsilateral caudal NTS showed positive Δfc_LBNP (Figure 3—figure supplement 1); however, we did not observe any functional connectivity changes with the CVLM. There is abundant evidence from animal experiments that the PVN projects heavily to the RVLM and Amb and to a lesser extent to Sp5 and reticular nuclei. These projections involve a wide variety of neurotransmitters including angiotensin-II, vasopressin, glutamate, and corticotropin-releasing hormone for RVLM, as well as oxytocin for the Amb (Loewy, 1981; Coote, 2004; Geerling et al., 2010; Sapru, 2013). Damage to the baroreflex arc elicits profound abnormalities in human blood pressure control (Biaggioni et al., 1994). Therapeutically, electrical baroreceptor stimulation has been tested for treating arterial hypertension and heart failure, although patients showed disparate treatment outcome (Heusser et al., 2016). Thus, a deeper knowledge of central baroreflex control may help understand inter-individual differences and identify patients most likely to respond to such therapies.

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The second hypothalamic system (Figure 2a +4d) involved the right anterior aspect of the LH/SON showing positive Δfc_LBNP with a small region in the ipsilateral dorsal medulla comprising the NTS, dorsal motor nucleus of the vagal nerve (DMN), and PCRt. These findings agree with animal experiments showing afferent connections of the LH/SON from the NTS (Sawchenko and Swanson, 1982; Card et al., 2011) and efferent projections to the NTS and DMN, the latter of which have been shown to be orexinergic (Allen and Cechetto, 1992; Coote, 2004). Their exact role in cardiovascular regulation, however, is still unclear. Potentially, this system also represents direct projections from the NTS to the SON that trigger the release of vasoactive hormones from the pituitary gland (Grindstaff and Cunningham, 2001). This pathway may complement neural cardiovascular regulation and serve as a backup for blood pressure maintenance in humans (Jordan et al., 2000). Clinically, hypotension-induced vasopressin release is attenuated in patients with neurodegenerative diseases affecting cardiovascular centres in the brainstem and hypothalamus (Puritz et al., 1983).

The third hypothalamic system involved the arcuate nucleus (Arc) (Figure 2e +4e), and showed positive Δfc_LBNP with three different medullary regions. The first was located in the right dorsal medulla comprising NTS, DMN, PCRt, IRt and dorsal paragigantocellular nucleus (DPGi). The second included the right IRt, right medullary reticular nucleus (MdRt) and midline ROb. And finally, a third small region spanned the left NTS/DMN with its rostrocaudal position closely matching that of the contralateral NTS observed for the ‘textbook’ baroreflex control system (Figure 3—figure supplement 1). Interestingly, the role of the Arc in cardiovascular regulation has received relatively little attention despite several lines of evidence. The Arc has long been known to receive afferent fibres from the NTS (Ricardo and Koh, 1978). Changes in blood pressure and vascular resistance during electric stimulation, Fos-like protein expression in response to prolonged hypotension and afferent renal nerve stimulation, and retrograde tracing studies link it to key cardiovascular organs (Li and Dampney, 1994; Sapru, 2013; Rahmouni, 2016). Furthermore, the Arc has been proposed as a potential region in which the leptin-melanocortin and renin-angiotensin-aldosterone systems interact to control sympathetic nerve activity (Hilzendeger et al., 2012), relevant to obesity-associated arterial hypertension (Greenfield et al., 2009). This issue is of utmost clinical relevance given the pandemic rise in the prevalence of obesity and associated cardiovascular disease.

This study combined functional connectivity and spectral analysis of fMRI signals to investigate hypothalamic regions involved during cardiovascular regulation. We found anatomically meaningful connectivity changes in five hypothalamic subregions during the cardiovascular challenge, all of which went along with spectral changes of the fMRI signal in the domains of low and high frequency cardiovascular regulation (LF,~0.1 Hz; HF,~0.28 Hz). In order to interpret these changes as BOLD signals, we need to assume that, at least for the HF domain, BOLD extends beyond the frequency range given by the canonical HRF (Figure 1i). Similar observations have recently been reported by several other groups (Chen and Glover, 2015; Gohel and Biswal, 2015; Lewis et al., 2016). However, caution is advisable, when interpreting results derived from BOLD signals. While the majority of our results were robust against the removal of physiological ‘noise’ signals, one should note that completely disentangling neuronal from non-neuronal BOLD signals is close to impossible for several reasons. Firstly, physiological noise is a mixture of several different noise sources, including those related to cardiac activity causing changes in arterial pulsatility, cerebral blood flow, cerebral blood volume and cerebrospinal fluid flow, and those related to respiration causing changes in the magnetic field and arterial carbon dioxide (Brooks et al., 2013). To complicate matters further, cardiac and respiratory activity are not independent but intricately linked through phenomena like the respiratory sinus arrhythmia. Secondly, several groups have recently shown that cerebral vascular regulation may be coordinated across long-distance brain regions, thus mimicking the structure of neuronal networks (Bright et al., 2020; Chen et al., 2020). Finally, every temporal regression of physiological ‘noise’ bears the risk of removing meaningful signal from the data (Iacovella and Hasson, 2011). Thus, the methodological approach used in this study cannot completely rule out the possibility that some of the connectivity we are seeing could be the result of highly structured physiological noise.

Nonetheless, we anticipate that the simultaneous in-vivo assessment of the different cardiovascular control systems in humans will deepen our understanding of these systems. Furthermore, access to individual neural signatures may facilitate development of novel pharmaceutical and electroceutical cardiovascular treatments, leading to an era of cardiovascular precision medicine.

The study took place at Hannover Medical School. It complied with the Declaration of Helsinki, and was approved by the local ethics committee (# 3404–2016). 22 healthy normotensive subjects (24 ± 5 years, 22.1 ± 3.1 kg/m², 123 ± 8/62 ± 7 mmHg, eight male) took part in the study after giving written informed consent and consent to publish their data anonimously.

We simulated orthostatic stress by means of lower body negative pressure (LBNP) (Goswami et al., 2008). Negative pressure of 30 mmHg was built up inside a custom-made polycarbonate chamber using a vacuum cleaner and a pressure gauge. The stimulation paradigm consisted of four alternating five-minute blocks, two with and two without negative pressure, starting with the negative pressure (Figure 1c). Functional MR images and physiological measures were continuously acquired during this cardiovascular challenge. Transitions between pressure states were excluded to avoid excessive movement. To quantify head motion, we calculated the root mean squares of relative image coordinate differences as transformed by the realignment matrices. We then checked for differences between fMRI runs with and without cardiovascular challenge by running a paired t-test.

Raw data as well as necessary scripts for reproducing the results of this study are available at Zenodo.

1. 1. Manuel J
   2. Färber N
   3. Gerlach DA
   4. Heusser K
   5. Jordan J
   6. Tank J
   7. Beissner F

   (2020) Zenodo

   fMRI during lower body negative pressure (LBNP) with concurrent physiological measurements.

   https://doi.org/10.5281/zenodo.3885042

1. 1. Allen GV
   2. Cechetto DF

   (1992) Functional and anatomical organization of cardiovascular pressor and depressor sites in the lateral hypothalamic area: I. descending projections

   The Journal of Comparative Neurology 315:313–332.

   https://doi.org/10.1002/cne.903150307

   • PubMed
   • Google Scholar
2. 1. Andersson JL
   2. Skare S
   3. Ashburner J

   (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging

   NeuroImage 20:870–888.

   https://doi.org/10.1016/S1053-8119(03)00336-7

   • PubMed
   • Google Scholar
3. 1. Avants BB
   2. Epstein CL
   3. Grossman M
   4. Gee JC

   (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain

   Medical Image Analysis 12:26–41.

   https://doi.org/10.1016/j.media.2007.06.004

   • PubMed
   • Google Scholar
4. 1. Beckmann CF
   2. Mackay CE
   3. Filippini N
   4. Smith SM

   (2009) Group comparison of resting-state FMRI data using multi-subject ICA and dual regression

   NeuroImage 47:S148.

   https://doi.org/10.1016/S1053-8119(09)71511-3

   • Google Scholar
5. 1. Beissner F
   2. Schumann A
   3. Brunn F
   4. Eisenträger D
   5. Bär KJ

   (2014) Advances in functional magnetic resonance imaging of the human brainstem

   NeuroImage 86:91–98.

   https://doi.org/10.1016/j.neuroimage.2013.07.081

   • PubMed
   • Google Scholar
6. 1. Beissner F

   (2015) Functional MRI of the brainstem: common problems and their solutions

   Clinical Neuroradiology 25:251–257.

   https://doi.org/10.1007/s00062-015-0404-0

   • PubMed
   • Google Scholar
7. 1. Benarroch EE

   (1993) The central autonomic network: functional organization, dysfunction, and perspective

   Mayo Clinic Proceedings 68:988–1001.

   https://doi.org/10.1016/S0025-6196(12)62272-1

   • Google Scholar
8. 1. Biaggioni I
   2. Whetsell WO
   3. Jobe J
   4. Nadeau JH

   (1994) Baroreflex failure in a patient with central nervous system lesions involving the nucleus tractus solitarii

   Hypertension 23:491–495.

   https://doi.org/10.1161/01.HYP.23.4.491

   • PubMed
   • Google Scholar
9. 1. Billman GE

   (2013) The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance

   Frontiers in Physiology 4:26.

   https://doi.org/10.3389/fphys.2013.00026

   • PubMed
   • Google Scholar
10. 1. Bright MG
    2. Whittaker JR
    3. Driver ID
    4. Murphy K

    (2020) Vascular physiology drives functional brain networks

    NeuroImage 217:116907.

    https://doi.org/10.1016/j.neuroimage.2020.116907

    • PubMed
    • Google Scholar
11. 1. Brooks JC
    2. Beckmann CF
    3. Miller KL
    4. Wise RG
    5. Porro CA
    6. Tracey I
    7. Jenkinson M

    (2008) Physiological noise modelling for spinal functional magnetic resonance imaging studies

    NeuroImage 39:680–692.

    https://doi.org/10.1016/j.neuroimage.2007.09.018

    • PubMed
    • Google Scholar
12. 1. Brooks JC
    2. Faull OK
    3. Pattinson KT
    4. Jenkinson M

    (2013) Physiological noise in brainstem FMRI

    Frontiers in Human Neuroscience 7:623.

    https://doi.org/10.3389/fnhum.2013.00623

    • PubMed
    • Google Scholar
13. Book

    1. Card JP
    2. Sved AF
    3. Pathways CA

    (2011)

    Central Regulation of Autonomic Functions

    Oxford University Press.

    • Google Scholar
14. 1. Chen JE
    2. Lewis LD
    3. Chang C
    4. Tian Q
    5. Fultz NE
    6. Ohringer NA
    7. Rosen BR
    8. Polimeni JR

    (2020) Resting-state "physiological networks"

    NeuroImage 213:116707.

    https://doi.org/10.1016/j.neuroimage.2020.116707

    • PubMed
    • Google Scholar
15. 1. Chen JE
    2. Glover GH

    (2015) BOLD fractional contribution to resting-state functional connectivity above 0.1 hz

    NeuroImage 107:207–218.

    https://doi.org/10.1016/j.neuroimage.2014.12.012

    • PubMed
    • Google Scholar
16. Book

    1. Coote JH

    (2004) The Hypothalamus and Cardiovascular Regulation

    In: Dun NJ, Machado BH, Pilowsky PM, editors. Neural Mechanisms of Cardiovascular Regulation. Springer. pp. 117–146.

    https://doi.org/10.1007/978-1-4419-9054-9_6

    • Google Scholar
17. 1. Dampney RA

    (1994) Functional organization of central pathways regulating the cardiovascular system

    Physiological Reviews 74:323–364.

    https://doi.org/10.1152/physrev.1994.74.2.323

    • PubMed
    • Google Scholar
18. 1. Dudás B

    (2013)

    The Human Hypothalamus - Anatomy, Functions and Disorders

    1–29, Anatomy of the Hyman Hypothalamus, The Human Hypothalamus - Anatomy, Functions and Disorders, Nova Science Publishers.

    • Google Scholar
19. 1. Geerling JC
    2. Shin JW
    3. Chimenti PC
    4. Loewy AD

    (2010) Paraventricular hypothalamic nucleus: axonal projections to the brainstem

    The Journal of Comparative Neurology 518:1460–1499.

    https://doi.org/10.1002/cne.22283

    • PubMed
    • Google Scholar
20. 1. Gohel SR
    2. Biswal BB

    (2015) Functional integration between brain regions at rest occurs in Multiple-Frequency bands

    Brain Connectivity 5:23–34.

    https://doi.org/10.1089/brain.2013.0210

    • Google Scholar
21. 1. Goswami N
    2. Loeppky JA
    3. Hinghofer-Szalkay H

    (2008) LBNP: past protocols and technical considerations for experimental design

    Aviation, Space, and Environmental Medicine 79:459–471.

    https://doi.org/10.3357/ASEM.2161.2008

    • PubMed
    • Google Scholar
22. 1. Greenfield JR
    2. Miller JW
    3. Keogh JM
    4. Henning E
    5. Satterwhite JH
    6. Cameron GS
    7. Astruc B
    8. Mayer JP
    9. Brage S
    10. See TC
    11. Lomas DJ
    12. O'Rahilly S
    13. Farooqi IS

    (2009) Modulation of blood pressure by central melanocortinergic pathways

    New England Journal of Medicine 360:44–52.

    https://doi.org/10.1056/NEJMoa0803085

    • PubMed
    • Google Scholar
23. 1. Grindstaff RR
    2. Cunningham JT

    (2001) Cardiovascular regulation of vasopressin neurons in the supraoptic nucleus

    Experimental Neurology 171:219–226.

    https://doi.org/10.1006/exnr.2001.7745

    • PubMed
    • Google Scholar
24. 1. Heusser K
    2. Tank J
    3. Brinkmann J
    4. Menne J
    5. Kaufeld J
    6. Linnenweber-Held S
    7. Beige J
    8. Wilhelmi M
    9. Diedrich A
    10. Haller H
    11. Jordan J

    (2016) Acute response to unilateral unipolar electrical carotid sinus stimulation in patients with resistant arterial hypertension

    Hypertension 67:585–591.

    https://doi.org/10.1161/HYPERTENSIONAHA.115.06486

    • PubMed
    • Google Scholar
25. 1. Hilzendeger AM
    2. Morgan DA
    3. Brooks L
    4. Dellsperger D
    5. Liu X
    6. Grobe JL
    7. Rahmouni K
    8. Sigmund CD
    9. Mark AL

    (2012) A brain leptin-renin angiotensin system interaction in the regulation of sympathetic nerve activity

    American Journal of Physiology-Heart and Circulatory Physiology 303:H197–H206.

    https://doi.org/10.1152/ajpheart.00974.2011

    • PubMed
    • Google Scholar
26. 1. Iacovella V
    2. Hasson U

    (2011) The relationship between BOLD signal and autonomic nervous system functions: implications for processing of "physiological noise"

    Magnetic Resonance Imaging 29:1338–1345.

    https://doi.org/10.1016/j.mri.2011.03.006

    • PubMed
    • Google Scholar
27. 1. Jenkinson M
    2. Bannister P
    3. Brady M
    4. Smith S

    (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images

    NeuroImage 17:825–841.

    https://doi.org/10.1006/nimg.2002.1132

    • PubMed
    • Google Scholar
28. 1. Jordan J
    2. Tank J
    3. Diedrich A
    4. Robertson D
    5. Shannon JR

    (2000) Vasopressin and blood pressure in humans

    Hypertension 36:e3–e4.

    https://doi.org/10.1161/01.HYP.36.6.e3

    • PubMed
    • Google Scholar
29. 1. Julien C

    (2006) The enigma of Mayer waves: facts and models

    Cardiovascular Research 70:12–21.

    https://doi.org/10.1016/j.cardiores.2005.11.008

    • PubMed
    • Google Scholar
30. 1. Kimmerly DS
    2. O'Leary DD
    3. Menon RS
    4. Gati JS
    5. Shoemaker JK

    (2005) Cortical regions associated with autonomic cardiovascular regulation during lower body negative pressure in humans

    The Journal of Physiology 569:331–345.

    https://doi.org/10.1113/jphysiol.2005.091637

    • PubMed
    • Google Scholar
31. 1. Lewis LD
    2. Setsompop K
    3. Rosen BR
    4. Polimeni JR

    (2016) Fast fMRI can detect oscillatory neural activity in humans

    PNAS 113:E6679–E6685.

    https://doi.org/10.1073/pnas.1608117113

    • PubMed
    • Google Scholar
32. 1. Li YW
    2. Dampney RA

    (1994) Expression of fos-like protein in brain following sustained hypertension and hypotension in conscious rabbits

    Neuroscience 61:613–634.

    https://doi.org/10.1016/0306-4522(94)90439-1

    • PubMed
    • Google Scholar
33. 1. Loewy AD

    (1981) Descending pathways to sympathetic and parasympathetic preganglionic neurons

    Journal of the Autonomic Nervous System 3:265–275.

    https://doi.org/10.1016/0165-1838(81)90068-0

    • PubMed
    • Google Scholar
34. Book

    1. Mai J
    2. Majtanik M
    3. Paxinos G

    (2016)

    Atlas of the Human Brain

    Academic Press.

    • Google Scholar
35. 1. Mark AL
    2. Mancia G

    (1983)

    Handbook of Physiology - the Cardiovascular System, Peripheral Circulation and Organ Blood Flow

    795–813, Cardiopulmonary baroreflexes in humans, Handbook of Physiology - the Cardiovascular System, Peripheral Circulation and Organ Blood Flow, John Wiley & Sons.

    • Google Scholar
36. 1. Moher Alsady T
    2. Blessing EM
    3. Beissner F

    (2016) MICA-A toolbox for masked independent component analysis of fMRI data

    Human Brain Mapping 37:3544–3556.

    https://doi.org/10.1002/hbm.23258

    • PubMed
    • Google Scholar
37. Book

    1. Paxinos G
    2. Xu-Feng H
    3. Sengul G
    4. Watson C

    (2012) Organization of Brainstem Nuclei

    In: Watson C, editors. The Human Nervous System. Academic Press. pp. 260–327.

    https://doi.org/10.1016/B978-0-12-374236-0.10008-2

    • Google Scholar
38. 1. Puritz R
    2. Lightman SL
    3. Wilcox CS
    4. Forsling M
    5. Bannister R

    (1983) Blood pressure and vasopressin in progressive autonomic failure: response to postural stimulation, l-dopa and naloxone

    Brain 106:503–511.

    https://doi.org/10.1093/brain/106.2.503

    • Google Scholar
39. 1. Rahmouni K

    (2016) Cardiovascular regulation by the arcuate nucleus of the hypothalamus

    Hypertension 67:1064–1071.

    https://doi.org/10.1161/HYPERTENSIONAHA.115.06425

    • Google Scholar
40. 1. Ricardo JA
    2. Koh ET

    (1978) Anatomical evidence of direct projections from the nucleus of the solitary tract to the Hypothalamus, Amygdala, and other forebrain structures in the rat

    Brain Research 153:1–26.

    https://doi.org/10.1016/0006-8993(78)91125-3

    • PubMed
    • Google Scholar
41. 1. Salman IM

    (2016) Major autonomic neuroregulatory pathways underlying short- and Long-Term control of cardiovascular function

    Current Hypertension Reports 18:18.

    https://doi.org/10.1007/s11906-016-0625-x

    • PubMed
    • Google Scholar
42. Book

    1. Saper CB
    2. Stornetta RL
    3. System CA

    (2015)

    The Rat Nervous System

    Academic Press.

    • Google Scholar
43. 1. Sapru HN

    (2013) Role of the hypothalamic arcuate nucleus in cardiovascular regulation

    Autonomic Neuroscience 175:38–50.

    https://doi.org/10.1016/j.autneu.2012.10.016

    • PubMed
    • Google Scholar
44. 1. Sawchenko PE
    2. Swanson LW

    (1981) Central noradrenergic pathways for the integration of hypothalamic neuroendocrine and autonomic responses

    Science 214:685–687.

    https://doi.org/10.1126/science.7292008

    • PubMed
    • Google Scholar
45. 1. Sawchenko PE
    2. Swanson LW

    (1982) The organization of noradrenergic pathways from the brainstem to the paraventricular and supraoptic nuclei in the rat

    Brain Research Reviews 4:275–325.

    https://doi.org/10.1016/0165-0173(82)90010-8

    • Google Scholar
46. 1. Smith SM

    (2002) Fast robust automated brain extraction

    Human Brain Mapping 17:143–155.

    https://doi.org/10.1002/hbm.10062

    • PubMed
    • Google Scholar
47. 1. Zhang Y
    2. Brady M
    3. Smith S

    (2001) Segmentation of brain MR images through a hidden markov random field model and the expectation-maximization algorithm

    IEEE Transactions on Medical Imaging 20:45–57.

    https://doi.org/10.1109/42.906424

    • PubMed
    • Google Scholar
48. 1. Zou QH
    2. Zhu CZ
    3. Yang Y
    4. Zuo XN
    5. Long XY
    6. Cao QJ
    7. Wang YF
    8. Zang YF

    (2008) An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF

    Journal of Neuroscience Methods 172:137–141.

    https://doi.org/10.1016/j.jneumeth.2008.04.012

    • PubMed
    • Google Scholar

Author details

1. Jorge Manuel

   Somatosensory and Autonomic Therapy Research, Institute for Neuroradiology, Hannover Medical School, Hanover, Germany

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Visualization, Methodology, Writing - original draft, Writing - review and editing, Data acquisition

   For correspondence

   ManuelSanchez.Jorge@mh-hannover.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1983-1448

2. Natalia Färber

   Somatosensory and Autonomic Therapy Research, Institute for Neuroradiology, Hannover Medical School, Hanover, Germany

   Contribution

   Data curation, Writing - review and editing, Data acquisition, Discussion of results

   Competing interests

   No competing interests declared

3. Darius A Gerlach

   Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany

   Contribution

   Methodology, Writing - review and editing, Discussion of results

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7044-6065

4. Karsten Heusser

   Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany

   Contribution

   Conceptualization, Methodology, Writing - review and editing, Discussion of results

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2571-5585

5. Jens Jordan

   1. Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany
   2. Chair of Aerospace Medicine, University of Cologne, Cologne, Germany

   Contribution

   Methodology, Writing - review and editing, Discussion of results

   Competing interests

   No competing interests declared

6. Jens Tank

   Institute of Aerospace Medicine, German Aerospace Center (DLR), Cologne, Germany

   Contribution

   Conceptualization, Supervision, Methodology, Writing - review and editing, Discussion of results

   Contributed equally with

   Florian Beissner

   Competing interests

   No competing interests declared

7. Florian Beissner

   Somatosensory and Autonomic Therapy Research, Institute for Neuroradiology, Hannover Medical School, Hanover, Germany

   Contribution

   Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Jens Tank

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0513-7551

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