The mechanism through which the gut microbiome exerts its effects on the CNS is multifactorial (neural, endocrine, and immunologic) but is thought to largely occur via the generation of bacterial metabolites, which exert their physiologic effects both locally and systemically. Short-chain fatty acids, produced by the bacterial fermentation of dietary carbohydrates, alter neuronal excitability31 and gut bacteria also manufacture a wide spectrum of neuroactive compounds that include dopamine, γ-aminobutyric acid, histamine, acetycholine and tryptophan, a precursor in the biosynthesis of serotonin32. The results of our work extend and build upon these findings by uncovering changes in white matter structural integrity and how they may be linked to specific gut microbiome populations. Other groups have previously linked the gut microbiome to structural changes in the brain33, and used machine learning methods to associate the gut microbiome with phenotypic data34; however, to the best of our knowledge, we are the first to utilize machine learning methods to directly link diet with gut microbiome populations and brain structure. Our findings associating gut microbiome populations to changes in brain structure are further buttressed with new evidence demonstrating the gut microbiome and Toll-like receptor 4 (TLR4) as critical stimulants of cerebral cavernous malformations35. Our method for uncovering potential links between gut microbiome populations and brain structural changes can help guide important new experiments to study how these microbiome populations impact the CNS beyond transiently modulating the presence and flux of neuroactive molecules and compounds.

Our observed structural changes may also be explained in part by recent studies demonstrating examples of how gut microbiome populations influence the transcriptional activity of genes involved in neuronal myelination36,37, which could potentially impart a lasting structural change and durable imprint on brain structure, function, and behavior. Intriguingly, all areas of significant FA change were the result of increased FA with no significant areas of decreased FA identified. While the cellular mechanisms for increases in FA remain unknown, increased water content in the myelin sheath, accelerated myelination and/or microscopic deficits of axonal structures or decreases in axonal diameter, packing density, and branching may all contribute to areas of elevated FA found in our study38,39. In addition, while there was an absence of significant FA differences between our high fat and standard diet groups, there were, however, substantial areas of significantly lowered AD, RD, and TR between these two groups (Fig. 1). While it is generally unexpected to observe an apparent decoupling in the direction of change in FA and AD as changes in either diffusion metric tends to follow the other, our results are not singularly unique as numerous prior reports have also made this observation40,41,42. With complex fiber architecture and subsequent orientation uncertainty, the direction of the measured tensor eigenvalues does not always correspond to the underlying structure, especially in instances where tensor measurement are being made in pathological tissue40,41. Additionally, different tensor shapes can yield a similar FA and the high degree of overlap between regions of concomitant reduction in both AD and RD may help explain the absence of FA differences in those regions between these two groups43. These results also highlight that even in the absence of a significant shift in FA, there are important changes occurring to the diffusion tensor that likely reflect important biological transformations.

In an extension of our work, we also performed a regression-based ensemble analysis of OTUs identified from sequenced 16S ribosomal RNA with calculated diffusion tensor measurements. From this analysis, we were able to identify unique populations of gut bacterial genera that were both associated with and predictive of ROI-specific tensor changes (Fig. 4). These associations were found to be independent of diet, which suggests that while the overall taxonomic composition and relative abundance of any one specific bacterial genera is diet-dependent, its contribution to underlying brain structure is not likely the result of synergistic effects derived from the presence of other bacterial populations or to behavioral changes that may result from a change in diet. This parallels the findings of numerous reports in the literature where the recolonization of a single gut bacterial species has been shown to be able to rescue perturbations in host physiology and ameliorate behavioral phenotypes2,8. With our Random Forest analysis not only predicting microbial driven ROI-specific changes but also changes to regions of the brain central to animal behavior including the neocortex, hypothalamus, and forebrain, these findings also highlight a potential mechanism whereby certain microorganisms are able to exert their systems-level behavioral effects. Notably, these predictions are again consistent with prior reports in the literature, with many of the bacterial genera identified in our analysis having been previously identified not only as important modulators of behavior but also having been shown to exert their effects in our predicted ROIs. A standout example of the predictive efficacy of our analysis is our identification of Roseburia being linked to microstructural changes in the neocortex (Fig. 4a). Previous reports have linked gut populations of Roseburia with elevated mood44, and with recent experimental findings implicating neocortical regions with altered mood and other affective disorders45, our analysis uncovers a potential hypothesis for how Roseburia is able to exert its specific behavioral effects. In sum, the methodology and results presented here are a novel framework with which one could potentially infer what brain structure may be given knowledge of the gut microbiome. In particular, our novel neuroimaging and machine learning classifier for the quantitative assessment of microbiome-brain region associations can guide future experimental work, whereby with an interest in a particular brain region or structure, our analysis can now allow for the selection of specific bacterial genera to generate a more tailored study of how specific gut microbiome populations impact brain structure, function, and behavior.

Our findings also bolster an emerging appreciation of metagenomic effects in experimental science. Particularly in experiments where mice and rats serve as a model organism, the absence of strong controls for metagenomic populations (such as the gut microbiome) may inadvertently confound experimental reproducibility as there are many potential linkages of the microbiome to variables known to influence experimental outcomes46. Our results now foster important experimental considerations for the neuroscience and neuroimaging community. Diffusion tensor imaging and the subsequent evaluation of white matter integrity are often employed in an effort to identify imaging endophenotypes across a broad range of neurologic and psychiatric diseases and the sensitivity of diffusion tensor imaging, coupled with its bias-free automated analysis, makes this an established and widespread clinical and experimental technique. Diffusion tensor techniques also serve as the basis for several large ongoing neuroimaging trials including the Alzheimer’s Disease Neuroimaging Initiative, the Human Connectome Project, and ENIGMA-DTI. With many clinical and experimental studies leveraging the utility and sensitivity of diffusion tensor imaging, that the gut microbiome could impart an unexpected impact on sensitive measures of diffusion tensor is an unanticipated and surprising challenge. While the effects of the gut microbiome are, in all likelihood, less likely to impact large well-organized white matter tracts such as the corpus callosum, studies employing DTI to identify novel imaging endophenotypes may be unknowingly affected by metagenomic effects that can further challenge efforts to identify and refine candidate neuroimaging biomarkers across a wide variety of diseases. This may be especially true for neuroimaging trials employing diffusion tensor techniques in schizophrenia, major depressive disorder, and bipolar disorder where investigators have yet to validate a truly robust neuroimaging biomarker that may have been influenced by unaccounted gut microbiome populations and their influence on the measurement of sensitive diffusion tensor indices.

In addition, concerns regarding the reproducibility of neuroimaging biomarkers have been raised with newly available data demonstrating the surprisingly poor reproducibility of candidate gene effects on imaging measures of mental illness. In an effort to countervail these findings, large imaging trials such as ENIGMA have pursued large sample sizes and meta-analyses as a means to screen for these false positive findings47; however, it is conceivable that these efforts could still be encumbered by the unaccounted contributions of gut microbiome populations on diffusion tensor metrics, thus masking and precluding the discovery of biologically important tensor changes and potentially salient neuroimaging biomarkers. These unforeseen potential challenges highlight a need to better understand the mechanism through which various bacterial genera are able to exert changes on brain structure and moreover, to uncover the mechanism by which they are able to do so in such a region-specific manner. These efforts will likely complement additional work exploring both the durability and malleability of these tensor changes and how these can be shaped not only by diet but also through the consumption of prebiotics, probiotics, and other means of dietary supplementation.