The Gut Microbiota in Neuroscience: A Narrative Review
on the Interaction between the Gut and Brain
Connectivity
Danique Mulder January 2021
Literature Thesis – Master Brain and Cognitive Science
Author: Danique Mulder
Supervisor: Alejandro Arias Vasquez (Radboud University Nijmegen) Co-assessors: Max Nieuwdorp & Madelief Wijdeveld
2 Table of Contents
Abstract ... 3
1. Introduction ... 4
2. Gut Microbiota and Functional Connectivity ... 7
2.1 Measuring functional connectivity ... 7
2.2 Functional connectivity and gut microbiota ... 7
3. Gut Microbiota and Structural Connectivity ... 13
3.1 Measuring structural connectivity ...13
3.2 Structural connectivity and gut microbiota ...13
4. Interventions ... 15
5. Mechanisms ... 17
6. Limitations and Future Directions ... 19
5.1 Well-powered studies and study comparability ...20
5.2 Include behavioral and cognitive measures ...20
5.3 Elucidate the mechanisms ...21
5.4 Data integration ...21
5.5 Improve interventions ...22
7. Conclusion ... 22
3 Abstract
Through years of research, it has been established that gut microbiota (GM) can affect emotional and cognitive functioning. As these functions likely arise from brain connectivity patterns, the gut microbiota may change functional outcomes by affecting functional and structural connectivity. As such, the GM could play a promising role in treating neurological and brain-related psychiatric disorders. However, the role of brain connectivity has to be further explained before developing these approaches. This review set out to answer this question by discussing studies investigating the relationship between gut microbiota and functional and structural connectivity. Overall, the gut microbiota seems to be associated with functional and structural connectivity. The complexity of this association becomes apparent from the high number of microbial genera and brain regions involved. In addition to observational evidence, gut microbiota-focused interventions elicited functional connectivity changes. However, it is still doubtful to what extend such interventions affect the GM composition itself. Although all evidence points in the same direction, it is still too early to draw clear conclusions. There are still many open-standing questions and methodological limitations to be solved. There are only a handful of studies conducted, generally with low statistical power and low mutual comparability. The research field is still young, and powerful integrative tools that allow studying the gut and brain in their full complexity are still developing.
4 1. Introduction
The brain consists of billions of neurons interconnected through axons. Neurons and neuronal populations that are spatially close have a higher probability of being connected than those remote. As a result, the brain structure represents a complex network with clusters of highly connected regions (i.e., structural connectivity) (1). Although not in a non-to-one fashion, those structural connections provide a basis for functional communication between brain areas (i.e., functional connectivity) (2,3). Such patterns of communication occur between fixed sets of brain regions, forming functional networks (4). Interestingly, such functional networks can be identified both during tasks and during rest.
There is overwhelming evidence that functional (resting-state) networks give rise to human cognitive functions. For example, the salience network is involved in the orientation of attention, and the default mode network (DMN) is linked to basal, stimulus-independent cognitive processes such as information integration and mind-wandering (2). Furthermore, patterns of functional connectivity can predict a range of cognitive outcomes, including sustained attention (5), executive functioning (6), and emotion regulation (7), suggesting that that individual differences in cognitive functions are, at least partially, the result of individual differences in functional connection strength within functional networks. Further evidence for the importance of functional networks in cognition comes from the field of psychopathology. Dysfunction in such networks is observed in increasing numbers of psychiatric disorders, including Attention Deficit/Hyperactivity Disorder (8), Schizophrenia (8), and Autism Spectrum Disorder (10).
The brains' functional and structural connectivity is affected by many different interacting factors. For example, genes appear to be a significant biological determinant of both brain structure and function: heritability studies have reported that additive genetic contribution may explain approximately 50 to 93 percent of the variance in structural connectivity (11) and 20 to 40 percent of the variance in functional connectivity (12,13). Likewise, the brain can change due to environmental factors. For example, a study by Scholz et al. reported that six weeks of juggling practice already resulted in stronger structural connectivity between areas that are functionally relevant to the skill (14). Similar findings have been reported for functional resting-state activity:
5 a sixteen-day working memory training increased resting-state functional connectivity within the DMN (15).
When discussing determinants of the brain, the gut-brain axis should not be ignored. This axis refers to the direct and indirect bidirectional pathways through which the gut and brain can communicate (Figure 1) and include endocrine, immune, and neural/vagal pathways (16). A key modifier of the gut-brain axis is the gut microbiota (GM), comprising all microorganisms (predominantly bacteria) in the intestines.
There are multiple ways through which the GM can modulate gut-to-brain signaling (Figure 1). Gut bacteria are involved in metabolizing otherwise indigestible dietary components. This process results in, amongst others, short-chain fatty acids such as butyrate, propionate, and acetate (17). These metabolites can be directly transported through the blood-brain barrier and used for energy production in the brain (18), but they may also affect the immune-pathway through their anti-inflammatory properties (19). Besides this indirect immune activation, gut bacteria can also directly affect immune cell populations (20). For example, the microglia – the cells that mediate the immune response within the central nervous system – are modulated by the GM, making it possible for gut bacteria to affect neuroinflammation directly (21). Finally, the GM can synthesize neuroactive compounds, such as the neurotransmitters serotonin, dopamine, norepinephrine, and gamma-aminobutyric acid (22). Within the gastrointestinal tract, these compounds play a role in controlling and maintaining homeostasis (23), but some studies have reported that they can also affect the central nervous system and thereby emotion and cognition (22).
The above-mentioned modulatory effects of the GM on the gut-brain axis show how the GM can indirectly affect the brain and influence cognitive and emotional processes. That the GM is indeed associated with cognition and emotion has been demonstrated many times. In healthy people, an association between the GM composition and mood was reported (24), and similar associations were observed in people with a dysbiotic GM. For instance, half of all patients with irritable bowel syndrome are also affected by a mood disorder (25), and patients with inflammatory bowel syndrome often have concomitant disturbances in their cognitive functioning (26). Finally, different psychiatric disorders, including Autism Spectrum Disorder (30) and Major Depressive
6 Disorder (28), have been linked to alterations in the GM composition, indicating that gut and brain functioning are also interconnected in brain-related diseases.
Presumably, the GM composition modulates gut-to-brain signaling, which in turn may affect the brain's structural and functional connectivity. Next, as a result of those connectivity changes, cognitive and emotional outcomes could be altered. What makes the GM interesting to study regardless of its indirect effects is that its composition can be modulated by environmental factors, including diet, lifestyle, life events, and antibiotics (17,29–31). As such, the GM could play a central role in treating or preventing brain and connectivity-related disorders. A better understanding of the GM-brain connectivity association and the involvement of the gut-brain signaling pathways in this association are necessary to achieve this.
This narrative review includes studies that investigated the GM composition together with brain connectivity. The first chapters discuss studies exploring the relationship between the GM composition and functional resting-state and structural connectivity. The third chapter elaborates on the status quo of the effect of microbial interventions on brain connectivity. This chapter is followed by a discussion of the role of the gut-brain axis in the GM-connectivity association. Finally, the last chapter will reflect on the results, identify gaps and provide recommendations for future research.
Figure 1. Involved Pathways in the Communication Between the Gut Microbiota and Brain (connectivity) through Modulation of the Gut-Brain Axis. Pathways include modulation of vagus nerve signaling and immune activation. External factors, including diet, antibiotics and exercise can alter the gut microbiota composition leading to changes in gut-synthesized neurotransmitters, short chain fatty acids (SCFAs) and immune cells. All these compounds have a putative effect on the brain and can effectuate changes in structural connectivity and functional connectivity.
7 2. Gut Microbiota and Functional Connectivity
2.1 Measuring functional connectivity
Resting-state networks consist of regions with high functional connectivity (FC) during rest. This functional connectivity can be measured using resting-state functional Magnetic Resonance Imaging (rs-fMRI). Rs-fMRI measures the Blood Oxygen Level Dependent (BOLD) signals throughout the brain. During rest, these signals exhibit low-frequency fluctuations. When regions communicate, these fluctuations synchronize. Therefore, resting-state FC can be computed as the temporal correlation of the low-frequency fluctuations between different regions (32). The strength of the temporal correlation corresponds to the FC strength. All studies discussed in this chapter used rs-fMRI to obtain resting-state FC strengths.
There are two commonly used methods to process the FC data: seed-based analysis and Independent Component Analysis. With seed-based methods, the BOLD signal of a predefined voxel or region is correlated to the signal of all other voxels or regions. This entails that the seed is manually set and that it is not possible to examine patterns on a whole-brain scale (2). Independent Component Analysis is a whole-brain data-driven approach for which the rs-fMRI signals are factorized into independent components. As a result, each component contains regions that have a strong FC. These components generally overlap with known resting-state networks (33). Because this method does not depend on a seed region, it is less prone to bias and can also estimate FC on a whole-brain scale (34).
2.2 Functional connectivity and gut microbiota
The FC strength would be a plausible mediator in the effect of the GM on cognitive and emotional outcomes. Nonetheless, only a handful of studies investigated the relationship between the GM composition and resting-state FC (see Table 1). Curtis et al. (35) examined how the GM diversity and composition were associated with seed-based resting-state FC of the insula in thirty healthy adults. The sample consisted of both smokers and non-smokers. The authors reported a positive correlation between GM diversity and the FC strength between the insula and frontal and cerebellar regions. Also, higher relative abundance of Prevotella and lower relative abundance of Bacteroides were associated with stronger overall insular FC. Notably, smoking was associated with
8 precisely this pattern: on average, smokers had a higher abundance of Prevotella and a lower abundance of Bacteroides. Although this study did not perform group analyses due to the small sample size, it would be plausible that smoking-induced changes in GM composition, which affected the insular FC strengths. What these taxonomic results mean in terms of cognition and emotion is unclear. There have been some reports of a lower relative Prevotella abundance in people with Attention Deficit/Hyperactivity Disorder (8), Parkinson's disease (36), and Autism Spectrum disorder (37), suggesting a possible beneficial effect of high Prevotella abundance. Conversely, although not often reported in relation to psychiatric and neurological disorders, high Prevotella abundance has been associated with inflammatory diseases (38).
In another study, Labus et al. (39) explored the association between the GM composition and resting-state FC. Sixty-five patients with irritable bowel syndrome (IBS) and 21 healthy adults were included. The analyses were limited to microbial genera of the Clostridiales order, and resting-state FC between brain regions involved sensorimotor functions. This study also assessed gastrointestinal sensorimotor functioning to explore if GM ~ FC associations are related to differences in gastrointestinal symptoms. Tripartite network analysis was performed to integrate data on the abundance of microbial genera, gastrointestinal sensorimotor functioning, and FC strengths (39). The analyses revealed numerous positive associations between Lachnospiraceae, Clostridium XIVa, and
Coprococcus and gastrointestinal sensorimotor functioning. These associations were
indirect through FC strength between sensory brain regions. Additionally, the associations were observed in healthy adults but were largely absent in IBS patients. One exception is the Roseburia genus: in IBS patients, there were multiple, mostly negative associations between this genus and FC, whereas these were absent in healthy adults. The authors argue that the functional connections between sensory brain regions could be protective and necessary for healthy gastrointestinal sensorimotor functioning. If microbial genera in the order of Clostridiales are imbalanced, this may disrupt the functional connections and thus the gastrointestinal sensorimotor functioning. As a result, IBS patients experience gastrointestinal discomfort, and healthy adults do not. Of course, IBS is a disease with a complex pathophysiology. Disruptions in FC and gastrointestinal functioning have a complex cause, of which GM dysbiosis is likely a contributing factor, but it is by no means the only.
Subjects N Measures Brain subset GM subset GM ~ FC associations Other References
Smokers and non-smokers
30 -rs-fMRI
GM fecal samples
Insular FC - High Prevotella and low Bacteroides abundance was associated with stronger insular FC
Smokers had high Prevotella and low Bacter-oides abundance as compared to non-smokers. Curtis et al. (2019) IBS patients and healthy adults 81 (66 / 21) -rs-fMRI -GM fecal samples -GI sensorimotor functioning tasks Sensory regions (S1, S2, M1, M2, mI, pI, Thalamus)
Clostridiales order
(Ruminococcaceae and Lachnospiraceae families)
-L. Incertae Sedis was positively associated
with S1 and rectal pain threshold in HC, but not in IBS.
-Coprococcus was negatively associated
with rectal discomfort through FC of the cautate in HC, but not in IBS.
-Clostridium XIVa was associated to rectal
discomfort and rectal pain threshold through Putamen and S1 FC and NAcc in HC, but not in IBS.
-Roseburia was negatively associated to FC
in IBS, but not in HC.
- Labus et al. (2016) ESRD patients and healthy adults 47 (28 /19) -rs-fMRI -GM fecal samples -Inflammatory Cytokines Examination -Neuropsychological testing
DMN - -Roseburia (↑ in HC) was positively
associated with pDMN-aDMN FC, partially mediated by levels of interleukin-6 (↑ in ESRD).
-Clostridium (↑ in HC) was positively
associated with cognitive functioning
-Prevotella (↑ in ESRD) was negatively
associated with cognitive functioning and DMN dissociation.
ESRD had ↓ cognitive functioning, ↑ systemic inflammation, dissociation of aDMN-pDMN and altered GM composition as compared to HC. Wang et al. (2019) Healthy female 57 -rs-fMRI -GM fecal samples FPN (left/right), ECN, DMN
- -Prevotella positively associated with FC in
aDMN; negatively associated with FC in pDMN.
-Bifidobacterium positively associated with
FC in aDMN and both FPNsBlautia negatively associated with FC in DMN -Blautia (↓) and Ruminococcae (↑) associated with FC between DMN and other networks.
Used linked ICA to simultaneously factorize variation in GM and variation in FC.
Kohn et al. (2020)
a/pDMN = anterior/posterior default mode network; ESDR = end-stage renal disease; ECN = executive control network; FC = functional connectivity; FPN = frontoparietal network; GM = gut microbiota; GI = gastrointestinal; HC = heathy control; IBS = irritable bowel syndrome; ICA = independent component analysis; m/pI = medial/posterior Insula; NAcc = Nucleus Accumbens; Rs-fMRI = resting-state functional Magnetic Resonance Imaging
10
Table 3. Summary of studies investigating the influence of probiotic interventions on brain functional and structural connectivity
Subjects N Measures Brain subset GM subset GM ~ SC associations Other References
39 (20 / 19) -DTI -GM fecal samples -Cognitive testing Amygdala, hypothalamus, thalamus, caudate, lenticular, frontal WM, occipitoparietal WM, subcortical WM
- GM composition was associated with white matter structure in obese and non-obese. The GM ~ SC associations were different in obese and non-obese adults, even though the GM
composition did not differ.
GM composition was associated with cognition Fernandez-Real et al. (2015)
Healthy adults 39 -DTI
-GM fecal samples -Depression and anxiety questionnaire -Emotional responses - GM profiling: high-Prevotella (n = 7) and high-Bacteroides (n = 32)
Weaker SC between emotional (amygdala, ACC) and BG regions, and between attentional (middle frontal gyrus) and sensory (right central sulcus) regions in the high-Bacteroides group as compared to the
high-Prevotella group. Higher negative emotional response in high-Prevotella group as compared to high-Bacteroides group Tillisch et al. (2017)
ACC = anterior cingulate cortex; BG = basal ganglia; DTI = Diffusion Tensor Imaging; GM = gut microbiota; SC = structural connectivity WM = white matter
Table 2.Summary of studies investigating the relationship between GM composition and SC
Subjects Intervention Measures Brain subset Brain results GM results Other Reference
Healthy adults Fermented milk containing strains from Bifidobacterium, Streptococcus and Lactobacillus (n = 12) or placebo (n = 11) (4 weeks) -Rs-fMRI -Task-based fMRI -GM fecal samples
Whole brain for task-based fMRI, rs-fMRI in key task related regions (i.e., PAG, insula, somatosensory cortex)
After four-week probiotic: -↑ emotional reactivity in PAG, insula and somatosensory cortex after probiotic. -↑ negative correlation between PAG-centered rsN and PAG reactivity
-↑ positive correlation between modulatory regions and PAG reactivity No changes in GM composition - Tillisch et al. (2012)
Healthy adults Probiotic containing strains from Bifidobacterium and
Lactobacillus (n = 15) or placebo (n = 15) (4 weeks) -Rs-fMRI -DTI -Task-based fMRI -Emotional decision-making, emotional memory -GM fecal samples
- After 4-week probiotic: -↓ FC in DMN, visual network, frontal pole, superior frontal gyrus -No changes in SC After 4-week probiotic: -No major differences in composition -↑ Bacteroides species -↑ Alistipe species After 4-week probiotic: -↑ emotional attention and memory for unpleasant stimuli -Correlation emotional memory and Bacteroides abundance Bagga et al. (2019)
DTI = Diffusion Tensor Imaging; DMN = default mode network; FC = functional connectivity; fMRI = resting-state functional Magnetic Resonance Imaging; GM = gut microbiota; PAG = periaqueductal gray; rs = resting state; rsN = resting state network; SC = structural connectivity
Obese and non-obese
11 The above-mentioned studies have explored the association between the GM composition and resting-state FC without exploring the mechanism that underlies this association. Wang et al. (40) explored the role of immune activation in the GM-FC association. The authors included 28 patients with end-stage renal disease and 19 healthy adults. The analyses were limited to FC within the DMN, and mediation analyses were performed to test if inflammation levels could mediate the relation between the microbiota and resting-state FC. Finally, neuropsychological tests were conducted to investigate if the gut-brain associations were related to cognitive functioning. In renal patients, all measures were different from healthy adults: patients had increased systemic inflammation, worse cognitive functioning, dissociation of the posterior and anterior DMN (pDMN-aDMN), and an altered GM composition. Likely, these alterations were the result of severely disrupted kidney function. The healthy GM composition was mainly enriched in Clostridium. This genus was associated with better cognitive functioning but not with DMN functional network properties. The GM composition in renal patients was primarily enriched in Prevotella. This abundance was associated with worse cognitive functioning and disturbed DMN network topology. Interestingly, there was one genus-FC association with a significant mediation effect of inflammation. The association between Roseburia – a genus more abundant in healthy adults than in renal patients – and pDMN-aDMN connectivity was mediated by levels of the pro-inflammatory cytokine Interleukin-6, in a way that higher Roseburia abundance was associated with lower levels of Interleukin-6 and stronger pDMN-aDMN functional connectivity. This suggests that the GM may affect the DMN by modulating the immune pathway of the gut-brain axis. Of note, the association between Roseburia and FC within the DMN was still present after taking this mediation into account, indicating that the immune system is not the only pathway through which the GM affects FC within the DMN.
The discussed studies used bivariate methods to analyze associations between the GM and the brain, but such methods do not consider the full complexity of both modalities. A multivariate integrative approach is necessary to identify complex interactions between the GM and brain connectivity. Additionally, the discussed studies investigated the relationship between GM and functional connectivity in different patient populations. As a reference, similar studies should be performed in a group of healthy subjects.
12 The study by Kohn et al. (41) is the first to address these gaps. The authors investigated associations between inter-individual variation in the GM composition and rs-FC in 57 healthy females. To do this, the linked Independent Component Analysis was applied. This is a multivariate alternative to the regular Independent Component Analysis that allows for simultaneous factorization of GM relative abundance data and brain resting-state FC strengths (42). The analysis included four canonical resting-state networks: the left and right frontoparietal attention network, executive control network, and the DMN. The results of this study show that some microbial genera are involved in multiple different resting-state functional networks. First, the Prevotella relative abundance was positively associated with FC strength variations within the posterior DMN but negatively associated with FC variability in the anterior DMN. Second, the relative abundance of the Bifidobacterium genus correlated with increased FC strength in both the anterior DMN and the two frontoparietal networks. Third, a high abundance of the Blautia genus was associated with decreased FC strength in the DMN. Finally, a low abundance of Blautia but a high abundance of Ruminococcae was related to strong FC strength between the DMN and other brain regions, and a low abundance of Blautia but a high abundance of Ruminococcae was related to strong FC strength between the DMN and other brain regions.
This study is the first to identify an association between Bifidobacterium and FC strength. This genus is important during development, and from a young age, it helps to fight off pathogens (43). With its health-promoting functions, it is one of the main targets in gut-brain and probiotic research. It would be interesting to see if future studies will replicate this association.
Interestingly, exploration of multivariate GM-brain associations in healthy subjects yields comparable results to previous studies. For example, associations with FC were again found for two genera of the order of Clostridiales (i.e., Blautia and
Ruminococcae). Noteworthy, Prevotella abundance again showed to be associated with
FC. Of all conducted studies, both Wang et al. (40) and Kohn et al. (44) linked Prevotella to resting-state FC within the DMN. The DMN is involved in basal cognitive processes, such as information integration and self-directed thought (2). Therefore, it is possible that Prevotella is indirectly involved in these processes – both in people with a balanced (41) and dysbiotic (40) state of the GM.
13 Overall, the GM is associated with the brain's resting-state FC. Various microbial genera were linked to FC between different brain regions. Of note, due to the small sample sizes, the statistical power was probably low. As a result, the generated picture is likely incomplete and may include some false positive results (45). Therefore, future research, in larger samples, has to replicate and expand on the current results. Additionally, different studies targeted different healthy or patient groups, complicating the integration of the results. Therefore, future research will also have to point out how evidence from various studies fits together.
3. Gut Microbiota and Structural Connectivity 3.1 Measuring structural connectivity
When millions of axons directly interconnect different neuronal clusters, they form axonal white matter tracts. The strength of those structural connections is determined by the number of axons that make up a white matter tract. These dense bundles of connections can be measured in vivo using MRI diffusion tensor imaging (DTI) (46). This technique computes structural connectivity (SC) based on the diffusivity of water molecules. If there are no obstacles, water molecules diffuse isotropically (i.e., diffusion is equal in all directions). If there are obstacles, the water molecules are forced to diffuse anisotropically: in a forced direction that aligns with the underlying structure. The orientation of axons, which have a coherent orientation, strongly influences the direction of diffusion (46). Hence, when inducing water diffusion by applying magnetic field gradients, the orientation and relative strength of the underlying white matter tracts can be reconstructed. Similar to rs-fMRI, diffusion MRI data can be processed using different analysis strategies: those that estimate the structure on a whole-brain scale or methods to assess a specific regions' connectivity strength (i.e., seed-based) (46).
3.2 Structural connectivity and gut microbiota
Although the brains' functional connectivity does not precisely follow the underlying structural connections, there is a robust association between the two (2,47). Therefore, investigating the brain's structural connections could add insight to our understanding of the GM-brain interactions. Unfortunately, the availability of studies that have explored the GM composition in relation to SC is minimal (see Table 2). Fernandez-Real et al. (48) investigated associations between GM composition, white matter
14 structure, and cognitive outcomes in 20 obese and 19 non-obese adults. The authors found no group differences between obese and non-obese in GM composition and SC, but the GM composition was associated with SC and cognition in both groups. Remarkably, the specific GM-SC associations varied strongly between obese and non-obese adults even though the GM composition did not differ between groups. For example, in non-obese adults, Streptococcus was associated with white matter structure in the caudate nucleus and the occipital lobe, but in obese adults, there were no associations between this genus and white matter structure. The same goes for GM-cognition associations: different genera were associated with cognition in obese and non-obese subjects. Hence, based on this study, the GM and SC seem to be associated. However, which microbial genera are involved in this association remains unclear.
Currently, there is only one other study that explored the association between the GM composition and SC. Tillisch et al. (49) investigated differences in whole-brain SC in subjects with different GM profiles. The associations between the GM, emotional responses, and depression and anxiety symptoms were also explored. Based on 39 healthy women's data, two microbial clusters were identified: a group with high
Prevotella abundance (n = 7) and a group with high Bacteroides abundance (n = 32). The Prevotella group had higher negative emotional responses than the Bacteroides group,
but there were no differences in positive emotional responses and depression/anxiety symptoms. Of note, there were no differences in negative emotional response when looking at the relative abundance of solely Bacteroides or Prevotella, highlighting the significance of the interaction between different microbial genera. Furthermore, it was demonstrated that SC could, with a modest accuracy of 67 percent, discriminate between the two GM profiles. In general, connections were weaker in the Bacteroides group. Connections with the most explanatory power were located between emotional (amygdala, ACC) and basal ganglia regions and between attentional (middle frontal gyrus) and sensory (right central sulcus) regions. The authors concluded that the GM profile is associated with SC in emotional, attentional, and sensory regions and that this association may affect (negative) emotional response. Previously, stronger connectivity within the cortico-limbic network (this network includes the basal ganglia, amygdala, ACC, and anterior insula) (66) and increased negative emotional responses (67) were reported in depression. Therefore, a high Prevotella-Bacteroides ratio could represent a vulnerability factor for developing depression. Of note, the high Prevotella group included
15 only seven participants, and the accuracy of the discriminative model is modest. Therefore, replication is recommended to see if the results are stable.
In conclusion, there is preliminary evidence for an association between the GM composition and SC. As studies did not focus on the same brain regions, microbial genera, and targeted different groups, it is difficult to infer how GM-SC and GM-FC associations are related. Moreover, the two studies included a small number of subjects. Another raised question concerns how the GM modulated the gut-brain axis. FC studies found the same GM-brain associations in patients and non-patients (see section 2.2) (40,50). The strength of the association was determined by the relative abundance rather than the specific microbial genera, suggesting the same underlying mechanisms. The study in obese and non-obese exploring SC reports the opposite: although obese and non-obese subjects had a comparable GM composition, the associations between GM and SC were not the same. These results indicate that even with a similar GM composition, the underlying mechanisms may differ. How the mechanisms may vary in different populations remains to be seen.
4. Interventions
It has been established that there is an observational association between GM composition and brain structural and functional connectivity. However, it is unknown to what extent this association can be manipulated by external interventions such as probiotic supplementation or dietary interventions. Additionally, it is unclear if the effect of GM-focused interventions on brain connectivity is sufficient to elicit changes in cognitive or emotional functioning. This chapter will discuss the current evidence for interventions that manipulate the GM to affect the brain, specifically focusing on brain structural and functional connectivity.
So far, it has been mostly preclinical studies that have shown how manipulating the GM composition can affect brain structure and function. Research in rodents has indicated that chronic probiotic administration can affect the expression of GABA receptors (51,52) and mediate anxiety-like behaviors via de vagus nerve (53). In addition, using an antibiotic cocktail, it was demonstrated that maturation and activation of microglia are regulated by the GM (21,54). Finally, strongly controlled dietary interventions were described to affect white matter SC through modulating the GM composition (55).
16 Preclinical studies suggest that manipulating the GM may be an effective approach to target the brain and brain dysfunctions, but there is a growing need to translate this preclinical work to human subjects. So far, human research has only explored the effects of probiotic interventions. In healthy subjects, multispecies probiotic interventions (containing strains from both Bifidobacterium and Lactobacillus genera) improved levels of anxiety (56–58), depression (57,58), cognitive reactivity to a sad mood (58,59), and emotional memory (58). Unfortunately, different studies use different probiotic supplements and different tests to measure the effects on cognition and behavior (60), and the increasing body of evidence that suggests a beneficial effect of probiotics is counter-balanced by studies that fail to find an effect (61–63)
If probiotics indeed affect cognition and emotion, this could occur through altering the underlying brain connectivity. To date, there have only been two studies that investigated the effects of probiotics on brain connectivity (see Table 3). First, Tillisch et al. (64) studied whether consumption of fermented milk with (n = 12) or without (n = 11) added probiotics (containing strains from Bifidobacterium, Streptococcus, and
Lactobacillus genera) for four weeks could alter task-based and resting-state FC. All
participants performed an emotional attention task during fMRI measurement to assess the emotional reactivity of specific brain regions. Probiotic supplementation resulted in reduced activity in a functional task-based network formed by the periaqueductal gray, insula, and somatosensory cortex. Moreover, probiotic intake was associated with changes in a periaqueductal gray-centered (i.e., the periaqueductal gray as a seed-region) resting-state network. Remarkably, even though task-based and resting-state activity changes were reported, there were no accompanying changes in the GM composition. With these results, this is the first study to demonstrate that probiotics may affect functional communication between brain regions, even without an accompanying change in GM composition. However, no cognitive testing was performed, so future research will point out if intervention-induced alterations in functional connectivity can modify the cognitive and emotional effects of probiotic supplementation.
Tillisch et al. focused exclusively on resting-state FC in regions involved in emotional reactivity, but the influence of probiotic supplementation on whole-brain resting-state FC remains unclear. Additionally, it is unknown if manipulating the GM composition can also modify the brains' structural connections. Bagga et al. (65) investigated the effect of four-week multi-strain probiotic supplementation on
whole-17 brain resting-state FC and SC. Thirty healthy adults received either a probiotic containing
Lactobacillus and Bifidobacterium strains or a placebo. Functional and structural
connectivity were compared pre- and post- intervention. The results revealed minor alterations in the GM composition: there were increases in species belonging to
Bacteroides and Alistipe after the probiotic intervention. Furthermore, reduced FC
strengths in regions of the DMN, visual network, frontal pole, and superior frontal gyrus were observed after the probiotic intervention, but no corresponding changes in SC were identified. The probiotic intervention also affected cognitive functioning, as there were improvements in both emotional attention and emotional memory, specifically for unpleasant situations. Additionally, emotional memory was positively correlated to
Bacteroides abundance, a finding that has already been reported in an earlier
observational study (49). In sum, this study shows beneficial effects of a multi-strain probiotic intervention on cognitive functioning, accompanied by changes in whole-brain resting-state FC. Unfortunately, the authors did not test the association between cognitive changes and changes in FC patterns, nor did they relate probiotic-induced changes in GM composition to changes in FC. Therefore, it is still unknown if changes in GM, connectivity, and cognition are genuinely interrelated.
In conclusion, preclinical studies have provided compelling evidence for the effectiveness of GM-focused interventions on the brain. However, evidence from human studies remains scarce. The conducted studies provided evidence that it is possible to modulate FC through probiotic interventions, even though there were no major changes in GM composition. The proof for a similar effect on SC is lacking. Of note, the evidence is based on only two studies, and there is an urgent need for replication. Besides, there are still open-standing questions. For example, current studies focus only on probiotic interventions, but the effects of other types of interventions (i.e., dietary interventions) are still unknown.
5. Mechanisms
The studies discussed so far have confirmed the association between the GM and brain functional and structural connectivity. The only way for the GM to affect the brain is through modulation of the gut-brain axis. Remarkably, only one study (40) explored this modulatory effect by measuring the gut-brain axis's immune activation pathway. With this, it provided evidence for the involvement of this pathway in the GM-FC
18 association. Although other studies did not include such measures, we can hypothesize about other possible gut-brain mechanisms based on the reported taxonomic findings.
Multiple studies have reported an association between the Prevotella-Bacteroides ratio and connectivity (35,49). Both Prevotella and Bacteroides are known to be involved in the production of short-chain fatty acids: GMs with high Prevotella abundance produce more propionate than the Bacteroides-dominated microbiota, and GMs dominated by
Bacteroides produce more butyrate (66). Prevotella and Bacteroides are not the only reported genera involved in the production of short-chain fatty acids. Roseburia, a genus from the Clostridiales order, also plays a role in Butyrate production.
SCFAs can regulate brain processes through direct and indirect pathways. For example, they can directly travel through the blood-brain barrier, where they can provide cells with energy or affect the maturation of microglia (i.e., the brains' immune cells) (19). Additionally, short-chain fatty acids can modulate the immune activation pathway of the gut-brain axis (19). Therefore, if there is a high abundance of genera that produce short-chain fatty acids, this could reduce inflammation levels and, through this pathway, affect the brain. Indeed, previous reports have described a relationship between butyrate production and cognitive functioning in animal models of Autism Spectrum Disorder (67,68). Unfortunately, studies connecting butyrate production, immune activation, and brain connectivity remain absent.
Besides the involvement of short-chain fatty acids, the current taxonomic findings point at a role for gut-synthesized neuroactive compounds. Genera within the order of
Clostridiales are involved in the biosynthesis of serotonin in the gut (69), and Bifidobacterium is involved in the gut synthesis of the neurotransmitter gamma
aminobutyric acid (GABA), which is the primary inhibitory CNS neurotransmitter (70,71).
There is evidence that gut-synthesized serotonin and GABA can exert an influence on the brain. Gut-synthesized serotonin has already been linked to mood and a range of cognitive functions in health and psychiatric disorders (for a review, see (72)). Additionally, disruptions in the GABA metabolism have been linked to anxiety and depression (73). Current evidence indicates that gut-synthesized neurotransmitters can affect the brain through a combination of different pathways, although there is still debate about the exact mechanism. It has been reported that both serotonin and GABA have an immune-modulatory role, through which levels of inflammation can be increased
19 or reduced (23,74). Additionally, recent reports suggest that GABA can even cross the blood-brain barrier in small quantities, where it can directly exert its influence on brain functioning (75). Finally, serotonin and GABA receptors have been located on vagal afferents to the central nervous system, proposing a role for the vagal signaling pathway (76,77).
In sum, based on the current taxonomic findings, the GM likely modulates levels of short-chain fatty acids, immune cells, and gut-synthesized neurotransmitters to affect brain functional and structural connectivity. Of note, the involvement of these pathways is speculative and needs to be directly tested in future research. Additionally, even if these pathways are indeed underlying the GM-connectivity association, this does not yet explain how these microscale changes in the brain are related to macroscale changes in functional and structural connectivity (78).
6. Limitations and Future Directions
Based on studies included in this review, the gut microbiota appears to be significantly associated with structural and functional connectivity. Thus far, all observational studies have found associations between the composition of the gut microbiota and brain connectivity, and interventional studies have reported significant effects of probiotics on functional but not on structural connectivity. Although an association was reported in all studies, the exact nature of the association is unknown: different studies report the involvement of different microbial genera and different brain regions. This is consistent with the complexity of the GM and brain connectivity and indicates that the two are interrelated through many complex multivariate interactions. Although the gut-brain signaling pathways through which the GM can affect brain connectivity have been scarcely studied, we can speculate about possible underlying mechanisms based on the taxonomic findings. So it is, for example, likely that the production of short-chain fatty acids and the gut-synthesized neurotransmitters play a role here. These compounds can activate the immune system, modulate vagal nerve activity, or even directly travel to the brain through blood circulation. How this would lead to alterations in brain connectivity is still unknown.
When interpreting the results, one should keep in mind that the evidence is scarce and based on only a handful of reports. At the current time, studies still have some limitations. As such, the evidence could be used as a framework for future studies, but
20 there is certainly still a long road ahead. The next sections discuss limitations, and several recommendations for future research are given.
5.1 Well-powered studies and study comparability
So far, most studies were executed with very different approaches: samples were drawn from different (patient) populations (e.g., healthy, IBS, obese), and analyses included different subsets of brain regions and microbial genera. Moreover, the sample sizes were generally small, likely resulting in low statistical power. There are many variables involved in gut-brain research (i.e., all microbial genera and all brain connections). Therefore, to obtain a large enough statistical power to identify associations, large sample sizes are required. With the small samples in the current studies, analyses may not detect all true associations, and reported associations could be false positives (45).
With such large differences between studies, combined with the small sample sizes, the outcomes are difficult to integrate. Future studies should focus on whole-brain connectivity to aid the comparability of the results. As this would also increase the number of variables, large sample sizes would be highly recommended.
5.2 Include behavioral and cognitive measures
Research points out that the gut microbiota can affect cognitive, emotional, and behavioral outcomes. Additionally, different microbial genera are associated with brain functional and structural connectivity. However, there has only been one study that explored all three components (i.e., GM, connectivity, and functional outcomes) in relation to one another (50). Especially with the goal to develop treatment and prevention approaches for psychiatric and neurological disorders, it would be essential to know if brain connectivity is indeed underlying the relationship between the gut and cognition, if the GM is affecting the connectivity-behavior association, or if there are other more significant mechanisms. A critical future step is to include functional measures on the level of behavior or cognition. Additionally, these measures should be integrated with the gut-connectivity analysis (for example, as described in section 5.4) to elucidate how the three are interrelated.
21 5.3 Elucidate the mechanisms
The underlying biological mechanisms of the interactions between gut microbiota and brain functional and structural connectivity in humans remain unclear. There have been numerous studies on possible mechanisms underlying gut-brain communication, both preclinical and in humans (reviewed in detail elsewhere, e.g., (16)). Usually, mechanisms are studied on a molecular level, without looking at the effects on the brain at a macro scale (e.g., connectivity). Strikingly, out of all available literature on the relation between brain connectivity and the GM, only one study explored an underlying gut-brain mechanism (40). The other mechanisms proposed in this review are speculative and await direct testing. Therefore, future research should measure inflammation, immune activation, neurotransmitters production, and other microbial metabolites that have a hypothesized role in gut-brain communication combined with GM and connectivity measures.
5.4 Data integration
Studies of the gut microbiota have pointed out an overwhelming diversity of interactions among gut bacteria and between the GM the brain. Although it has been acknowledged that microbiota-gut-brain interactions are complex and can likely not be reduced to simple bivariate associations (i.e., associations between one bacterial strain and one brain connection), this is exactly what most discussed studies did. As a result, information concealed in the relationship between multiple variables (i.e., interaction effects) is lost (79). Remarkably, there is only one study that has opted for a multivariate integrative approach (41). In this study, the authors identified multiple multivariate associations between different clusters of microbial genera and functional connections. Such multivariate and multimodal associations cannot be detected with the common bivariate approaches.
Future studies should invest their resources into integrating data from the gut-microbiota and brain connectivity. One possible approach is the Linked Independent Component analysis (as applied in (41)), which allows for simultaneous factorization of data from different modalities. Another interesting approach is the systems biology approach. This interdisciplinary approach focuses on multivariate interactions in biological systems rather than exploring each modality in parallel (80). There are already different systems biology software programs available (81). However, they are mainly
22 utilized to study protein interactions and, so far, there is little involvement of macroscale neuroscientific measures. Nevertheless, the field of human connectomics has already been proposed as an extension of systems biology to macroscale neuroscience (82–84) and could provide insight into the complex microbiota-gut-brain interactions.
5.5 Improve interventions
Most studies conducted thus far have been observational, and more intervention studies are necessary to establish directionality and assess the effectiveness of GM-focused treatment approaches. Such interventions seem to affect brain resting-state functional connectivity and cognitive and emotional functioning based on existing evidence. The multi-strain probiotics used in these interventions contain strains from
Lactobacillus and Bifidobacterium genera. These genera are among the more abundant
within the gut (85) and already play an important role during development (17,43). As such, they are commensal and have no known pathogenic effects (86). Studies using probiotic mixtures containing strains from these two genera have not yet reached a consensus on the effects on mood and cognition (60).
Based on the observational evidence, there are other microbial genera with a putative beneficial effect on the brain that may be worth exploring. Many of those genera may have both beneficial and unfavorable effects on the brain depending on the strain. Therefore, a comprehensive description of the GM on strain-level is necessary to deepen the understanding of its effect on the gut-brain axis.
The majority of the current studies use 16S rRNA gene sequencing analysis (87), but this technique does not have sufficient power to distinguish between different bacterial species and strains. Over the past years, metagenomics has become a key method for the analysis of the gut microbiota. This technique provides higher taxonomical resolution and can inform about the GM composition on species- and strain-levels. Therefore, this method could be implemented in future research to obtain a more fine-grained image of the microbiota-gut-brain axis.
7. Conclusion
The studies discussed in this review demonstrate that the human gut microbiota is significantly associated with brain structural and functional connectivity. Additionally, interventional research points to a possible effect of GM-focused interventions on
23 functional communication between brain regions and cognitive functioning. These are promising findings for developing therapeutic approaches that manipulate the GM to treat or prevent neurological and psychiatric disorders. However, there is still a long road ahead. Future studies should attempt to integrate different components of the microbiota-gut-brain axis with brain connectivity to create a comprehensive picture of the role of the gut microbiota in brain health and disease.
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