Elevated BMI impacts brain-state dynamics within the sensorimotor-to-transmodal hierarchy
Jinfeng Han
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorKaixiang Zhuang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
Search for more papers by this authorDebo Dong
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorYingkai Yang
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorYong Liu
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorQinghua He
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
Search for more papers by this authorTingyong Feng
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorXu Lei
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorJiang Qiu
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
Search for more papers by this authorCorresponding Author
Hong Chen
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
Correspondence
Hong Chen, Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China.
Email: [email protected]
Search for more papers by this authorJinfeng Han
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorKaixiang Zhuang
Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
Search for more papers by this authorDebo Dong
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorYingkai Yang
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorYong Liu
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorQinghua He
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
Search for more papers by this authorTingyong Feng
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorXu Lei
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
Search for more papers by this authorJiang Qiu
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
Search for more papers by this authorCorresponding Author
Hong Chen
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
Correspondence
Hong Chen, Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China.
Email: [email protected]
Search for more papers by this authorJinfeng Han and Kaixiang Zhuang contributed equally to this study.
Abstract
Objective
Overweight and obesity, as commonly indicated by a higher BMI, are associated with functional alterations in the brain, which may potentially result in cognitive decline and emotional illness. However, the manner in which these detrimental impacts manifest in the brain's dynamic characteristics remains largely unknown.
Methods
Based on two independent resting-state functional magnetic resonance imaging data sets (Behavioral-Brain Research Project of Chinese Personality, n = 1923; Human Connectome Project, n = 998), the current study employed a Hidden Markov model to identify the spatiotemporal features of brain activity states. Subsequently, the study examined the changes in brain-state dynamics and the corresponding functional outcomes that arise with an increase in BMI.
Results
Elevated BMI tends to shift the brain's activity states toward a greater emphasis on a specific set of states, i.e., the metastate, that are relevant to the joint activities of sensorimotor systems, making it harder to transfer to the metastate of transmodal systems. These findings were reconfirmed in a longitudinal sample (Behavioral-Brain Research Project of Chinese Personality, n = 34) that exhibited a significant increase in BMI at follow-up. Importantly, the alternation of brain-state dynamics specifically mediated the relationships between BMI and adverse functional outcomes, including cognitive decline and symptoms of mental illness.
Conclusions
The altered brain-state dynamics within the sensorimotor-to-transmodal hierarchy provide new insights into obesity-related brain dysfunctions and mental health issues.
CONFLICT OF INTEREST STATEMENT
The authors declared no conflict of interest.
Open Research
DATA AVAILABILITY STATEMENT
Anonymized Behavioral-Brain Research Project of Chinese Personality (BBP) data are available from the corresponding author upon reasonable request. Human Connectome Project (HCP) data are publicly available at the following link: https://db.humanconnectome.org/.
Supporting Information
| Filename | Description |
|---|---|
| oby23933-sup-0001-supinfo.docxWord 2007 document , 1.8 MB | Supplementary Figure S1. Evaluations for the optimal number of brain states. (A) Free energies of five hidden Markov model (HMM) runs across 5–12 number of states (K). (B) Model evaluation metrics based on stability of HMM runs. (C) Model evaluation metrics based on model generalizability between split-half data. The higher these similarity measures are, the better the model quality is. Supplementary Figure S2. The clustering result in human connectome project (HCP) dataset. Based on the maximum loadings of fractional occupancy (FO) values, 12 brain states are also clustered into two sets in the HCP dataset. Supplementary Figure S3. Resampling-based comparisons of fractional occupancy (FO) values between individuals with overweight/obesity and control groups based on different numbers of brain state (K). (A) Resampled mean difference values across 8 brain states and two composite values of metastates in the BBP dataset. (B) Resampled mean difference values across 10 brain states and two composite values of metastates in the BBP dataset. (C) Resampled mean difference values across 8 brain states and two composite values of metastates in the human connectome project (HCP) dataset. (D) Resampled mean difference values across 10 brain states and two composite values of metastates in the HCP dataset. Brain states are arranged from smallest to largest of the resampled mean difference values. † uncorrected p < 0.05; * false discovery rate (FDR) q < 0.05; ** FDR q < 0.01; *** FDR q < 0.001. Supplementary Figure S4. Associations of body mass index (BMI), fractional occupancy (FO) values of various substates, and multiple behavioral measures in human connectome project (HCP) dataset. The heatmap illustrates the correlations between FO values in different substates and various behavioral measures. Based on the correlation between variables, we performed hierarchical clustering separately on the X-axis and Y-axis. The asterisk (*) denotes significance at the false discovery rate (FDR) q < 0.05 level. The black box indicates that the corresponding mediation relationship is significant at the FDR q < 0.05 level. That is, FO values of the corresponding substates can mediate the relationship between BMI and behavioral variables. The analysis leads to the following key observations: (1) Cognitive and emotional issues are the variables most affected by brain states, a conclusion highly consistent with the principal analysis results (Figure 5). (2) The proportions of brain states are interrelated, and the way they influence variables strictly follows their respective metastases. (3) While the impact of substates on variables is widespread, substates 1, 9, and 12 have the most pronounced effects, being able to explain the majority of functional consequences related to BMI. This finding resonates with the principal analysis results (Figure 3). Supplementary Table S1. Correlation between various obesity-related indicators and fractional occupancy (FO) values of metastates across two independent datasets. |
Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
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