Brain Structure and Response to Emotional Stimuli as Related to Gut Microbial Profiles in Healthy Women. Issue 8 (October 2017)

• Record Type: Journal Article
• Authors: Tillisch, Kirsten; Mayer, Emeran A.; Gupta, Arpana; Gill, Zafar; Brazeilles, Rémi; Le Nevé, Boris; van Hylckama Vlieg, Johan E.T.; Guyonnet, Denis; Derrien, Muriel; Labus, Jennifer S.
• Is Part Of: Psychosomatic medicine. Volume 79:Issue 8(2017)
• Publication Date: 2017-10
• Subjects: brain-gut axis -- hippocampus -- microbiota -- mind-body -- negative affect -- neuroimaging -- BMI = body mass index -- BOLD = blood oxygen level dependent -- CD-HIT = cluster database at high identity with tolerance -- CNS = central nervous system -- CT = cortical thickness -- DTI = diffusion tensor imaging -- FA = flip angle -- fMRI = functional MRI -- FOV = field of view -- GMV = gray matter volume -- HAD = Hospital Anxiety And Depression Scale -- MC = mean curvature -- MRI = magnetic resonance imaging -- PANAS = Positive Affect Negative Affect Schedule -- rRNA = ribosomal ribonucleic acid -- SD = standard deviation -- sPLS-DA = sparse partial least squares discriminant analysis -- SPM = statistical parametric mapping -- TE = echo time -- TR = repetition time -- VIP = variable importance in projection; Medicine, Psychosomatic -- Periodicals; 616.0805
• DOI: 10.1097/PSY.0000000000000493
• Languages: English
• ISSNs: 0033-3174

Abstract

ABSTRACT: Objective: Brain-gut-microbiota interactions may play an important role in human health and behavior. Although rodent models have demonstrated effects of the gut microbiota on emotional, nociceptive, and social behaviors, there is little translational human evidence to date. In this study, we identify brain and behavioral characteristics of healthy women clustered by gut microbiota profiles. Methods: Forty women supplied fecal samples for 16S rRNA profiling. Microbial clusters were identified using Partitioning Around Medoids. Functional magnetic resonance imaging was acquired. Microbiota-based group differences were analyzed in response to affective images. Structural and diffusion tensor imaging provided gray matter metrics (volume, cortical thickness, mean curvature, surface area) as well as fiber density between regions. A sparse Partial Least Square-Discrimination Analysis was applied to discriminate microbiota clusters using white and gray matter metrics. Results: Two bacterial genus-based clusters were identified, one with greater Bacteroides abundance ( n = 33) and one with greater Prevotella abundance ( n = 7). The Prevotella group showed less hippocampal activity viewing negative valences images. White and gray matter imaging discriminated the two clusters, with accuracy of 66.7% and 87.2%, respectively. The Prevotella cluster was associated with differences in emotional, attentional, and sensory processing regions. For gray matter, the Bacteroides cluster showed greater prominence in the cerebellum, frontal regions, and the hippocampus. Conclusions: These results support the concept of brain-gut-microbiota interactions in healthy humans. Further examination of the interaction between gut microbes, brain, and affect in humans is needed to inform preclinical reports that microbial modulation may affect mood and behavior. Abstract : Supplemental digital content is available in the text.