Abstract
The brain is the most complex organ in the human body, interacting with every other major organ system to continuously maintain homeostasis. Thus it is not surprising that the brain also interacts with our microbiota, the trillions of bacteria and other organisms inhabiting the ecosystem of the human being. As we gather knowledge about the way that our microbiota interact with their local environments, there is also increasing interest in their communication with the brain.
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Keywords
- Positron Emission Tomography
- Irritable Bowel Syndrome
- Fractional Anisotropy
- Diffusion Tensor Imaging
- Blood Oxygen Level Dependent
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Introduction
The brain is the most complex organ in the human body, interacting with every other major organ system to continuously maintain homeostasis. Thus it is not surprising that the brain also interacts with our microbiota, the trillions of bacteria and other organisms inhabiting the ecosystem of the human being. As we gather knowledge about the way that our microbiota interact with their local environments, there is also increasing interest in their communication with the brain.
Brain-Gut Communication
Bidirectional communication between the brain and gut has been well described (Fig. 18.1) [1–4]. The brain communicates with the gut via the autonomic nervous system (particularly the vagus nerve) and the hypothalamic-pituitary adrenal axis. Descending monoaminergic pathways also act on the dorsal horn and can regulate gut-related sensations. Gastrointestinal motility, secretion, local blood flow, and immune regulation are modulated by the brain, generating stereotypic patterns of gut response which are context specific, such as the classic gastrointestinal stress response of nausea and/or fecal urgency. Thus the local environment of gastrointestinal microbes is continuously adjusted by central influences. These interactions provide a partial explanation for the differences in gut bacterial populations between healthy persons and those with gastrointestinal illness [5–7] or prolonged psychological stress [8]. Similarly, preclinical studies have identified altered fecal bacteria after experimental pre and post-natal stress [9–12].
Completing the bidirectional loop, the brain receives afferent input from the gut, likely from a variety of pathways, as described below. With a surface area far exceeding that of the skin, the gut is the largest interface between the body and the external environment, and contains the body’s most numerous population of microbes. The gut also has a vast immune system and complex nervous system through which the microbiota can communicate with the brain. Biologically active compounds such as serotonin, histamine [13], catecholamines [14], gamma-aminobutyric acid (GABA) [15], and others can be produced in various amounts by specific bacteria. Additionally, organisms can stimulate the release of these compounds by gut enterochromaffin cells, leading to central signaling and clinically apparent symptoms [16]. An example of this is the central nausea induced at the nucleus tractus solitarius after rotavirus-stimulated gastrointestinal serotonin release [17]. An alternate pathway by which information may reach the brain from the gut is via neurochemicals secreted into the portal venous system, as is seen in hepatic encephalopathy [18, 19].
The vagus nerve has been shown to be essential in some but not all preclinical studies of microbe-brain interactions and likely plays a key role in the microbe-gut-brain axis (MGBA) in humans [20, 21]. Interoceptive (internal) signals of body state are relayed from vagal and spinal afferent nerves to the brain stem and then for further processing in higher cortical centers [22, 23]. It has been proposed that interoceptive input has relevance beyond merely reporting the homeostatic “status” of the body. In the model proposed by Craig and others, interoceptive signals appear to be integrated with emotional and cognitive input primarily in the anterior insula. This combined input is used continuously to create a sense of momentary “self” which can be consciously interpreted as happy, sad, healthy, ill, etc. [24, 25]. Since visceral feedback from the gut and other body sites contributes to our conscious state of wellbeing, it then follows that the gut’s luminal organisms also have the opportunity to influence mood states like anxiety or depression [26, 27]. Given the difficulty of gaining access to the cellular workings of the brain in humans, neuroimaging has emerged as a tool to increase our understanding of the MGBA. In the section below, several of the key imaging modalities will be reviewed and their integration into analyses of the MGBA will be discussed.
Neuroimaging in Humans
Functional Neuroimaging Techniques
One of the most common research techniques used to image changes in brain function between groups or after a treatment intervention is functional magnetic resonance imaging (fMRI). This technique is non-invasive, safe, and easy to perform. Functional MRI measures changes in the percentage of oxygenated versus deoxygenated hemoglobin, taking advantage of the differing magnetic properties of the molecules. During an experimental task, when a brain region is more active compared to a baseline or control task, blood flow increases and thus a higher proportion of oxygenated hemoglobin is observed in that area. This change in the regional magnetic properties is measured as the blood oxygen level dependent (BOLD) signal by the scanner and provides an indirect measurement of a change in brain activity. Functional MRI has fairly good spatial resolution of 2–4 mm but does not have the precision of post-mortem studies in animals. Functional MRI has been used successfully to identify differences in brain function in gastrointestinal disease states, such as irritable bowel syndrome and inflammatory bowel disease, as well as in healthy people before and after chronic ingestion of probiotics [28–30].
The other common mode of functional neuroimaging is Positron Emission Tomography (PET). Radiolabeled chemicals are injected into the blood stream and PET measures the emissions regionally throughout the brain. PET has the advantage of measuring physiologic processes more directly via the use of radio-labeled ligands; however it has the drawback of being more invasive and requires radiation exposure. Radioligand PET can be used to explore baseline interactions between regional brain distribution of a variety of signalling systems (including dopamine [31, 32], serotonin [33], substance P/neurokinin-1 [34, 35]) with gut microbiome and metabolomic profiles, as well as assess pre- to post-intervention changes in the MGBA after intervention with specific probiotics. While PET imaging is more invasive and difficult to perform, it has the advantage over fMRI of isolating specific biological processes or pathways for measurement.
The Functional Imaging of the Gut-Brain Axis
The brain-gut axis has been examined using fMRI and PET in humans, particularly in the setting of evoked pain, or anticipation to pain in the esophagus and distal colon. Alterations in resting brain function have also been described in patients with functional gastrointestinal disorders, which are believed to involve brain-gut axis dysfunction [36–38]. Whether these resting brain signal changes represent ongoing gastrointestinal input to the brain or persistent changes in the function of neural circuitry due to chronic disease is not yet known.
Functional MRI has been extensively used to observe changes in brain response after a treatment intervention, most commonly using pharmaceuticals or behavioral interventions, but little has been done to image the effects of antibiotics, probiotics, or dietary interventions in humans [39–41]. Only one study to date has described functional brain changes in response to a probiotic intervention [29]. In this study healthy, normal weight women without any gastrointestinal symptoms, pain or psychiatric disorder, were randomized to treatment with a probiotic, a placebo dairy product or no treatment. The response to an emotional attention task was measured with fMRI before and after the treatment period and the probiotic group showed reductions in response to the emotional task, suggestive of reduced vigilance to negative emotional stimuli. This difference in brain activity was not correlated to any subject reports of mood or gastrointestinal symptoms. Evaluation of the microbiota in that study confirmed that the experimental probiotic could be identified in the stool of the probiotic ingesting subjects but did not show group specific changes in the overall architecture of the microbiota. This is consistent with other studies and suggests that microbial metabolites rather than overall microbial configuration may be the salient result of probiotic ingestion [42]. This initial study suggests that subtle changes in the gut contents can lead to measureable changes in brain function, even in the absence of a conscious awareness of the change. Future studies, which may be able to use microbiome composition, along with metabolomic and metagenomic measurements from stool to correlate with brain function at baseline or after a probiotic intervention, will lead to a better understanding of how the MGBA can be modulated in health and disease.
Structural Neuroimaging
In addition to functional neuroimaging, advances in MR imaging of gray and white matter structure have proven valuable in describing group differences in psychiatric illness and chronic pain syndromes compared to healthy populations. Differences in both white matter and gray matter have been identified in irritable bowel syndrome and functional dyspepsia, both of which are considered to be disorders of the brain-gut axis and which likely are accompanied by alterations in the gut microbiota [43–51]. High resolution structural brain images can be used to produce global (whole-brain), regional, and voxel-level indices of gray matter density and volume as well as cortical thickness, surface area and mean curvature (Fig. 18.2). Network analysis from graph theory has recently been applied to gray matter morphometry to demonstrate alterations in regional topology, providing strong evidence for extensive structural reorganization of cortical and subcortical regions previously implicated in altered brain responses to visceral pain stimuli and their expectation [43]. The biological substrate underlying grey matter changes may involve increased or decreased glial cells, changes in dendritic spines or synapses or less likely, neural degeneration. Gray matter has been shown to remain quite plastic even during adulthood [53–55]. The effects of peripheral factors such as the microbiota on gray matter structure is likely most profound during development, and has been shown in rodent models [56]. However, given that alterations in brain function and behavioral symptom changes occur in response to probiotic interventions in adults, it is likely that structural changes will follow.
Another MRI-based modality of assessing brain structure is diffusion tensor imaging (DTI), which allows the evaluation of white matter integrity and anatomy. DTI can assess the connectivity between gray matter regions via white matter tracts, measuring the fiber pathways that support functional networks. Two main types of DTI analyses are frequently performed [57]. In the first, white matter tract integrity is measured, most commonly expressed as fractional anisotropy (FA), although additional measurements, such as radial or mean diffusivity are also used. This technique assesses the diffusivity of water in the brain tissue. Water molecules unconstrained by cellular architecture, such as in the CSF, freely move in all directions (isotropic) and thus have a FA value of 0. However, water molecules in dense, parallel white matter tracts containing axons are constrained and have high FA values. Decreases in the FA of white matter tracts can indicate decreased axonal number, myelin integrity, or axonal cytoskeleton integrity. The other DTI analysis method, tractography, allows quantification of fiber density between brain regions, and is commonly used to describe limited or whole brain networks.
It has yet to be clearly defined whether the differences in brain structure in disorders of the brain-gut axis are a result of the chronic condition or a predisposing factor, though there is a great likelihood that both pathways occur. Associations between brain structure and microbiota profiles have not yet been described but provide an opportunity to better understand the interactions between the luminal contents and the brain.
Neuroimaging in Animals
Imaging the brain in animals is also achieved with MRI and PET, as well as more direct radiotracer studies. Rodent fMRI and PET provide fair spatial and temporal resolution but require restraint and/or sedation of the animal to avoid movement, which may confound the interpretation of the functional results. Autoradiography allows neuroimaging in non-sedated, nonrestrained animals. A radiotracer is injected and after the experiment the animal is sacrificed and the brain is cryosectioned to identify regional tracer uptake, allowing a very detailed view of the involved neural circuitry [58]. Using animal imaging in parallel with modulation of the microbiota is likely to inform human studies as animal studies allow for the control of more variables and ability to perform post-mortem studies of the brain.
Incorporation of Behavioral and Gastrointestinal Measurements to Neuroimaging Studies
Preclinical studies have been useful in identifying potential behavioral and peripheral measures that are of particular relevance in examining the MGBA. Modulation of gastrointestinal flora in rodents by using specific bacterial strains, antibiotics, or by using germ-free animals has shown associations with anxiety-like behavior across multiple paradigms [20, 21, 56, 59, 60]. Rodent models of anxiety-like behavior are well developed and show responses to pharmacological agents, such as selective serotonin reuptake inhibitors, indicating the presence of relevant shared core neural circuitry with humans. In humans, measures of anxiety and depression including clinical diagnosis, trait measures and psychological symptoms correlate with brain structure and function [61–63]. Similar to the findings in rodent models, the ingestion of a Bifidobacterium and Lactobacillius containing probiotic in healthy humans showed diminished psychological symptoms, including anxiety symptoms in a placebo controlled randomized clinical trial [64]. The central mechanisms through which these symptoms change can be probed with neuroimaging, using symptom measures as covariates. In addition to looking at the interactions between psychological symptoms and brain function when modulating the microbiota in clinical trials, additional gastrointestinal measures such as intestinal permeability, immune activation, motility and visceral sensitivity will be useful in better elucidating gut to brain communication.
Evaluating the MGBA in the Era of Big Data
The ability to analyze the large datasets produced by neuroimaging studies and microbiota profiling has been advancing rapidly [65]. While studies evaluating effects of single organisms or probiotic consortia on the brain will continue to be of great interest; the emerging use of systems biology approaches to the understanding of the relationship between complex structural and functional neural networks and the microbiome is likely to advance our understanding of the MGBA tremendously [66]. Both the microbiome and the brain act within integrated networks for which classical hypothesis driven analytic approaches are not ideal. Agnostically applied multivariate analysis techniques are being used to identify neural networks to develop biomarkers of complex diseases, such as chronic pain, anxiety and depression. These approaches can be utilized to combine complex imaging datasets with genomic, metagenomic and metabolomic data to study the interaction between neural and microbial networks [67]. Since current evidence suggests that the gastrointestinal microflora are likely to play a role in the development and persistence of these disorders, it will be important to look at the interactions between brain phenotypes and the gut microbiome.
Limitations in Neuroimaging of the MGBA
In both the imaging of animal and human MGBA there are a number of limitations. In animals, we have the ability to meticulously manage the presence or absence of specific microorganisms, we are able to image the brain in both direct and indirect ways, and we can observe the effects of various environmental pressures on the developing animal. However, we are faced with the difficulty of translating the relevance of behavior from rodent models to humans, and must deal with the clear differences in the brain between species. As stated by Craig, “A rat is not a monkey is not a human” [68]. He and others [69] have described the difficulties of the bench to clinical translation with a particular focus on interoception and pain processing, but similar arguments can be made for the study of the stress response, emotion and cognition. If an animal model, as Craig describes in the case of the rodent, lacks the anterior insular cortex, the site in which our subjective sense of physical wellbeing may arise, and if the basic pathways through which the visceral afferents communicate with emotional and cognitive centers vary, then our animal models of complex phenomena must be interpreted with caution.
In humans on the other hand, we have great limitations in our ability to study all three branches of the MGBA precisely. Our access to the gut is limited and most data samples are collected non-invasively, via the stool. This allows us to examine the gut microbiome in broad strokes, but does not differentiate between the luminal and mucosal environment, much less local microenvironments or regional differences throughout the gut [70, 71]. In humans the effects of diet, medications, and external stressors on microbiota content, gastrointestinal motility and immune function are difficult to account for even in the most carefully controlled experiments. Additionally, it is likely that many of the MGBA pathways affected by the microbiota are established early in life, while the brain has its most rapid and dramatic remodeling [72]. Despite these concerns, the combination of human and animal imaging, using a translational or reverse-translational model [73–75] may prove to be the most effective and flexible strategy in evaluating the role of the gut microbiome in brain function, mood and cognition.
Conclusion
Neuroimaging of the MGBA is in its infancy but will clearly be an important modality on the road to understanding the role of microbes in many aspects of health and disease. The current focus on disorders of gastrointestinal disease, such as inflammatory or function bowel diseases, is already shifting to the study of anxiety and depression, metabolic diseases and neurologic disease. With this shift, incorporation of neuroimaging techniques will allow us to measure the rich connectivity between three complex systems: the microbiota, gut and brain.
Abbreviations
- BOLD:
-
Blood oxygen level dependent
- DTI:
-
Diffusion tensor imaging
- FA:
-
Fractional anisotropy
- fMRI:
-
Functional magnetic resonance imaging
- GABA:
-
Gamma-aminobutyric acid
- HPA:
-
Hypothalamic-pituitary-adrenal
- MGBA:
-
Microbe-gut-brain axis
- MRI:
-
Magnetic resonance imaging
- PET:
-
Positron emission tomography
References
Mayer EA (2011) Gut feelings: the emerging biology of gut-brain communication. Nat Rev Neurosci 12(8):453–466
Mayer EA, Naliboff BD, Craig AD (2006) Neuroimaging of the brain-gut axis: from basic understanding to treatment of functional GI disorders. Gastroenterology 131(6):1925–1942
Hornby PJ (2001) Receptors and transmission in the brain-gut axis. II. Excitatory amino acid receptors in the brain-gut axis. Am J Physiol Gastrointest Liver Physiol 280(6):G1055–G1060
Aziz Q, Thompson DG (1998) Brain-gut axis in health and disease. Gastroenterology 114(3):559–578
Nistal E et al (2012) Differences in faecal bacteria populations and faecal bacteria metabolism in healthy adults and celiac disease patients. Biochimie 94(8):1724–1729
Carroll IM et al (2012) Alterations in composition and diversity of the intestinal microbiota in patients with diarrhea-predominant irritable bowel syndrome. Neurogastroenterol Motil 24(6):521–530, e248
Andoh A et al (2012) Multicenter analysis of fecal microbiota profiles in Japanese patients with Crohn’s disease. J Gastroenterol 47(12):1298–1307
Knowles SR, Nelson EA, Palombo EA (2008) Investigating the role of perceived stress on bacterial flora activity and salivary cortisol secretion: a possible mechanism underlying susceptibility to illness. Biol Psychol 77(2):132–137
Bailey MT, Coe CL (1999) Maternal separation disrupts the integrity of the intestinal microflora in infant rhesus monkeys. Dev Psychobiol 35(2):146–155
Bailey MT, Lubach GR, Coe CL (2004) Prenatal stress alters bacterial colonization of the gut in infant monkeys. J Pediatr Gastroenterol Nutr 38(4):414–421
O’Mahony SM et al (2009) Early life stress alters behavior, immunity, and microbiota in rats: implications for irritable bowel syndrome and psychiatric illnesses. Biol Psychiatry 65(3):263–267
Garcia-Rodenas CL et al (2006) Nutritional approach to restore impaired intestinal barrier function and growth after neonatal stress in rats. J Pediatr Gastroenterol Nutr 43(1):16–24
Thomas CM et al (2012) Histamine derived from probiotic Lactobacillus reuteri suppresses TNF via modulation of PKA and ERK signaling. PLoS One 7(2):e31951
Asano Y et al (2012) Critical role of gut microbiota in the production of biologically active, free catecholamines in the gut lumen of mice. Am J Physiol Gastrointest Liver Physiol 303(11):G1288–G1295
Barrett E et al (2012) Gamma-Aminobutyric acid production by culturable bacteria from the human intestine. J Appl Microbiol 113(2):411–417
Uribe A et al (1994) Microflora modulates endocrine cells in the gastrointestinal mucosa of the rat. Gastroenterology 107(5):1259–1269
Hagbom M et al (2011) Rotavirus stimulates release of serotonin (5-HT) from human enterochromaffin cells and activates brain structures involved in nausea and vomiting. PLoS Pathog 7(7):e1002115
Butterworth RF (2013) The liver-brain axis in liver failure: neuroinflammation and encephalopathy. Nat Rev Gastroenterol Hepatol 10(9):522–528
Lesniewska V et al (2006) Effect on components of the intestinal microflora and plasma neuropeptide levels of feeding Lactobacillus delbrueckii, Bifidobacterium lactis, and inulin to adult and elderly rats. Appl Environ Microbiol 72(10):6533–6538
Bravo JA et al (2011) Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad Sci U S A 108(38):16050–16055
Bercik P et al (2011) The intestinal microbiota affect central levels of brain-derived neurotropic factor and behavior in mice. Gastroenterology 141(2):599–609, 609 e1–e3
Craig AD (2009) How do you feel–now? The anterior insula and human awareness. Nat Rev Neurosci 10(1):59–70
Craig AD (2002) How do you feel? Interoception: the sense of the physiological condition of the body. Nat Rev Neurosci 3(8):655–666
Craig AD (2010) The sentient self. Brain Struct Funct 214(5–6):563–577
Craig AD (2011) Significance of the insula for the evolution of human awareness of feelings from the body. Ann N Y Acad Sci 1225:72–82
Bajaj JS et al (2013) Modulation of the metabiome by rifaximin in patients with cirrhosis and minimal hepatic encephalopathy. PLoS One 8(4):e60042
Dinan TG, Stanton C, Cryan JF (2013) Psychobiotics: a novel class of psychotropic. Biol Psychiatry 74:720–726
Tillisch K, Mayer EA, Labus JS (2011) Quantitative meta-analysis identifies brain regions activated during rectal distension in irritable bowel syndrome. Gastroenterology 140(1):91–100
Tillisch K et al (2013) Consumption of fermented milk product with probiotic modulates brain activity. Gastroenterology 144(7):1394–1401, 1401 e1–e4
Agostini A et al (2011) Brain functional changes in patients with ulcerative colitis: a functional magnetic resonance imaging study on emotional processing. Inflamm Bowel Dis 17(8):1769–1777
Mukherjee J et al (2002) Brain imaging of 18F-fallypride in normal volunteers: blood analysis, distribution, test-retest studies, and preliminary assessment of sensitivity to aging effects on dopamine D-2/D-3 receptors. Synapse 46(3):170–188
Olsson H, Halldin C, Farde L (2004) Differentiation of extrastriatal dopamine D2 receptor density and affinity in the human brain using PET. Neuroimage 22(2):794–803
Paterson LM et al (2013) 5-HT radioligands for human brain imaging with PET and SPECT. Med Res Rev 33(1):54–111
Sprague DR et al (2007) Human biodistribution and radiation dosimetry of the tachykinin NK1 antagonist radioligand [18F]SPA-RQ: comparison of thin-slice, bisected, and 2-dimensional planar image analysis. J Nucl Med 48(1):100–107
Jarcho JM et al (2013) Diminished neurokinin-1 receptor availability in patients with two forms of chronic visceral pain. Pain 154(7):987–996
Hong JY et al (2013) Patients with chronic visceral pain show sex-related alterations in intrinsic oscillations of the resting brain. J Neurosci 33(29):11994–12002
Zeng F et al (2011) Abnormal resting brain activity in patients with functional dyspepsia is related to symptom severity. Gastroenterology 141(2):499–506
Van Oudenhove L et al (2010) Abnormal regional brain activity during rest and (anticipated) gastric distension in functional dyspepsia and the role of anxiety: a H(2)(15)O-PET study. Am J Gastroenterol 105(4):913–924
Tillisch K et al (2008) Studying the brain-gut axis with pharmacological imaging. Ann N Y Acad Sci 1144:256–264
Wise RG, Tracey I (2006) The role of fMRI in drug discovery. J Magn Reson Imaging 23(6):862–876
Mayer EA et al (2002) The effect of the 5-HT3 receptor antagonist, alosetron, on brain responses to visceral stimulation in irritable bowel syndrome patients. Aliment Pharmacol Ther 16(7):1357–1366
McNulty NP et al (2011) The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Sci Transl Med 3(106):106ra106
Labus J et al (2014) Irritable bowel syndrome in female patients is associated with alterations in structural brain networks. Pain 155:137–149
Holzschneider K, Mulert C (2011) Neuroimaging in anxiety disorders. Dialogues Clin Neurosci 13(4):453–461
Ayling E et al (2012) Diffusion tensor imaging in anxiety disorders. Curr Psychiatry Rep 14(3):197–202
May A (2011) Structural brain imaging: a window into chronic pain. Neuroscientist 17(2):209–220
Ellingson BM et al (2013) Diffusion tensor imaging detects microstructural reorganization in the brain associated with chronic irritable bowel syndrome. Pain 154:1528–1541
Zeng F et al (2013) Regional brain structural abnormality in meal-related functional dyspepsia patients: a voxel-based morphometry study. PLoS One 8(7):e68383
Seminowicz DA et al (2010) Regional gray matter density changes in brains of patients with irritable bowel syndrome. Gastroenterology 139(1):48–57 e2
Moayedi M et al (2011) Contribution of chronic pain and neuroticism to abnormal forebrain gray matter in patients with temporomandibular disorder. Neuroimage 55(1):277–286
Jiang Z et al (2013) Sex-related differences of cortical thickness in patients with chronic abdominal pain. PLoS One 8(9):e73932
Irimia A, Van Horn JD (2012) The structural, connectomic and network covariance of the human brain. Neuroimage 66C:489–499
Anderson BJ (2011) Plasticity of gray matter volume: the cellular and synaptic plasticity that underlies volumetric change. Dev Psychobiol 53(5):456–465
May A (2011) Experience-dependent structural plasticity in the adult human brain. Trends Cogn Sci 15(10):475–482
Gustin SM et al (2012) Pain and plasticity: is chronic pain always associated with somatosensory cortex activity and reorganization? J Neurosci 32(43):14874–14884
Sudo N et al (2004) Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J Physiol 558(Pt 1):263–275
Jellison BJ et al (2004) Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. AJNR Am J Neuroradiol 25(3):356–369
Wang Z et al (2013) Alterations in prefrontal-limbic functional activation and connectivity in chronic stress-induced visceral hyperalgesia. PLoS One 8(3):e59138
Matthews DM, Jenks SM (2013) Ingestion of Mycobacterium vaccae decreases anxiety-related behavior and improves learning in mice. Behav Processes 96:27–35
Neufeld KA et al (2011) Effects of intestinal microbiota on anxiety-like behavior. Commun Integr Biol 4(4):492–494
Drevets WC (2003) Neuroimaging abnormalities in the amygdala in mood disorders. Ann N Y Acad Sci 985:420–444
Montag C et al (2013) Imaging the structure of the human anxious brain: a review of findings from neuroscientific personality psychology. Rev Neurosci 24(2):167–190
Keedwell PA, Linden DE (2013) Integrative neuroimaging in mood disorders. Curr Opin Psychiatry 26(1):27–32
Messaoudi M et al (2011) Assessment of psychotropic-like properties of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in rats and human subjects. Br J Nutr 105(5):755–764
Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186–198
Hulshoff Pol H, Bullmore E (2013) Neural networks in psychiatry. Eur Neuropsychopharmacol 23(1):1–6
Gonzalez I et al (2012) Visualising associations between paired ‘omics’ data sets. BioData Min 5(1):19
Craig AD (2009) A rat is not a monkey is not a human: comment on Mogil (Nature Rev. Neurosci. 10, 283–294 (2009)). Nat Rev Neurosci 10(6):466
Mogil JS, Davis KD, Derbyshire SW (2010) The necessity of animal models in pain research. Pain 151(1):12–17
Li X et al (2011) A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface. PLoS One 6(11):e26542
Belkaid Y, Naik S (2013) Compartmentalized and systemic control of tissue immunity by commensals. Nat Immunol 14(7):646–653
Stiles J, Jernigan TL (2010) The basics of brain development. Neuropsychol Rev 20(4):327–348
Sinha R, Shaham Y, Heilig M (2011) Translational and reverse translational research on the role of stress in drug craving and relapse. Psychopharmacology (Berl) 218(1):69–82
Holschneider DP, Bradesi S, Mayer EA (2011) The role of experimental models in developing new treatments for irritable bowel syndrome. Expert Rev Gastroenterol Hepatol 5(1):43–57
Becerra L et al (2013) Parallel buprenorphine phMRI responses in conscious rodents and healthy human subjects. J Pharmacol Exp Ther 345(1):41–51
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Tillisch, K., Labus, J.S. (2014). Neuroimaging the Microbiome-Gut–Brain Axis. In: Lyte, M., Cryan, J. (eds) Microbial Endocrinology: The Microbiota-Gut-Brain Axis in Health and Disease. Advances in Experimental Medicine and Biology(), vol 817. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0897-4_18
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