Gut-Brain Axis in Taiwan: A Perspective from Task-engaged fMRI and Microbial Compositions
Poster No:
1043
Submission Type:
Abstract Submission
Authors:
Yu-He Wu1, Chih-Mao Huang2, Yu-Tang Tung3, Yi-Ping Chao4, Changwei Wu5
Institutions:
1Taipei Medical University, Taipei, Taiwan, 2National Yang Ming Chiao Tung University, Hsinchu, Taiwan, 3National Chung Hsing University, Taichung, Taiwan, 4Chang Gung University, Taoyuan, Taiwan, 5Taipei Medical University, New Taipei, Taiwan
First Author:
Co-Author(s):
Introduction:
Gut-brain axis, an intricate network linking gut flora and central nervous system, is essential for maintaining homeostasis and profoundly impacts cognitive function [1–2]. While majority of literature focused on brain functionality in specific populations (e.g., individuals with cirrhosis, malnutrition, or those undergoing post-bariatric surgery and physical activity [3–6]) or under a steady resting-state [7], ignoring the fact that task engagements better reflect the cognitive functions. In this work, we explored the gut-brain axis among Taiwanese young adults through the associations between gut flora composition and task-based brain activities, and its relationship with trait questionnaire of sleep.
Methods:
A total of 52 healthy participants (24 males, 28 females), aged 20–35 years (mean: 24.1 ± 3.8), were recruited without history of neurological or psychiatric disorders and maintained drug abstinence for at least three months prior to the experiment.
Participants completed two cognitive tasks, Face-Shape matching task and Numeric-Stroop task (details in Fig.1), while undergoing fMRI scanning. Imaging data were preprocessed using CONN toolbox, followed by first- and second-level analyses in SPM12. Task-related regions of interest (ROIs) were identified through one-sample t-tests, and beta values from these ROIs were extracted based on AAL template using MarsBaR.
Gut microbiota profiling included stool sample collection prior to the MRI scans, with microbial composition and relative abundance analyzed through 16S rRNA sequencing. Correlation analyses were performed between microbial composition and beta values from task-based ROIs.
Two questionnaires, Pittsburgh Sleep Quality Index (PSQI) and State-Trait Anxiety Inventory (STAI), were recorded before MRI scans and integrated into regression models to control the trait covariates in microbial-neural associations during task engagements.
Participants completed two cognitive tasks, Face-Shape matching task and Numeric-Stroop task (details in Fig.1), while undergoing fMRI scanning. Imaging data were preprocessed using CONN toolbox, followed by first- and second-level analyses in SPM12. Task-related regions of interest (ROIs) were identified through one-sample t-tests, and beta values from these ROIs were extracted based on AAL template using MarsBaR.
Gut microbiota profiling included stool sample collection prior to the MRI scans, with microbial composition and relative abundance analyzed through 16S rRNA sequencing. Correlation analyses were performed between microbial composition and beta values from task-based ROIs.
Two questionnaires, Pittsburgh Sleep Quality Index (PSQI) and State-Trait Anxiety Inventory (STAI), were recorded before MRI scans and integrated into regression models to control the trait covariates in microbial-neural associations during task engagements.
·Fig.1 Flow chart for the entire study design.
Results:
In Numeric-Stroop task, significant activation was observed in regions associated with Dorsal Attention Network (DAN), particularly middle frontal cortex (MFC) and inferior frontal gyrus (IFG), reflecting inhibitory control functions. In Face-Shape matching task, prominent activation was detected in limbic system, specifically amygdala and hippocampus, which are linked to emotional memory processing. (Fig.2)
Correlation analyses revealed distinct relationships between microbial family relative abundance and ROIs activity. Specifically, Rikenellaceae showed a significant negative correlation with activity in left hippocampus (r = -0.32, R² = 0.101, p = 0.020) and left amygdala (r = -0.30, R² = 0.093, p = 0.027). Similarly, Bacteroidaceae exhibited a significant negative correlation with activity in right MFC (r = -0.32, p = 0.017) and right IFG (r = -0.35, p = 0.009). (Fig.2)
Furthermore, multiple regression models incorporating questionnaires were applied. Notably, controlling sleep efficiency (PSQI component 4) enhanced adjusted R² (left hippocampus: R² = 0.143, p = 0.010; left amygdala: R² = 0.163, p = 0.006), making microbial-neural associations more pronounced. In contrast, no questionnaire-based covariates were found to strengthen relationships between Bacteroidaceae and DAN-associated ROIs.
Correlation analyses revealed distinct relationships between microbial family relative abundance and ROIs activity. Specifically, Rikenellaceae showed a significant negative correlation with activity in left hippocampus (r = -0.32, R² = 0.101, p = 0.020) and left amygdala (r = -0.30, R² = 0.093, p = 0.027). Similarly, Bacteroidaceae exhibited a significant negative correlation with activity in right MFC (r = -0.32, p = 0.017) and right IFG (r = -0.35, p = 0.009). (Fig.2)
Furthermore, multiple regression models incorporating questionnaires were applied. Notably, controlling sleep efficiency (PSQI component 4) enhanced adjusted R² (left hippocampus: R² = 0.143, p = 0.010; left amygdala: R² = 0.163, p = 0.006), making microbial-neural associations more pronounced. In contrast, no questionnaire-based covariates were found to strengthen relationships between Bacteroidaceae and DAN-associated ROIs.
·Fig.2 Glass brain and Scatter plots for results.
Conclusions:
Here, we opened a new perspective to study the gut-brain axis, because both brain and microbiota are time-varying. We elucidated neural-microbial interactions, particularly in cognition-related ROIs, under task performances. Currently, the study is limited to analyzing gut flora at the Family level, without controlling the diet habits, and the brain template for ROI extraction needs to further tested. We welcome future collaboration from different dietary cultures, which will provide new insights into gut-brain axis in terms of cognition and emotion.
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 2
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI) 1
Keywords:
Cognition
Emotions
FUNCTIONAL MRI
Memory
Sleep
Other - Microbiota
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Cryan, J. (2012) Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci 13, 701–712.
3. Ahluwalia, V. (2016) Impaired Gut-Liver-Brain Axis in Patients with Cirrhosis. Scientific Reports, 6(1).
4. Shennon, I. (2024) The infant gut microbiome and cognitive development in malnutrition. Clinical Nutrition, 43(5), 1181–1189.
5. Xiang, Q. (2024) Multi-omics insights into the microbiota-gut-brain axis and cognitive improvement post-bariatric surgery. Journal of Translational Medicine, 22(1).
6. Schrenk, S. J. (2023) Impact of an online guided physical activity training on cognition and gut-brain axis interactions in older adults: protocol of a randomized controlled trial. Frontiers in Aging Neuroscience, 15.
7. Zhang, S. (2022). Brain network topology and Structural–Functional connectivity coupling mediate the association between gut microbiota and cognition. Frontiers in Neuroscience, 16.