Cognitive Flexibility and Overeating Tendency: Behavioural and Neural Insights
Poster No:
723
Submission Type:
Abstract Submission
Authors:
Zihao Tang1,2, Tao Chen1,3, Kyi Nue Nyo Zin4, Muireann Irish1,3, Chenyu Wang1,2,4
Institutions:
1Brain and Mind Centre, The University of Sydney, Sydney, Australia, 2Central Clinical School, The University of Sydney, Sydney, Australia, 3School of Psychology, The University of Sydney, Sydney, Australia, 4Sydney Neuroimaging Analysis Centre, Sydney, Australia
First Author:
Zihao Tang
Brain and Mind Centre, The University of Sydney|Central Clinical School, The University of Sydney
Sydney, Australia|Sydney, Australia
Brain and Mind Centre, The University of Sydney|Central Clinical School, The University of Sydney
Sydney, Australia|Sydney, Australia
Co-Author(s):
Tao Chen
Brain and Mind Centre, The University of Sydney|School of Psychology, The University of Sydney
Sydney, Australia|Sydney, Australia
Brain and Mind Centre, The University of Sydney|School of Psychology, The University of Sydney
Sydney, Australia|Sydney, Australia
Muireann Irish
Brain and Mind Centre, The University of Sydney|School of Psychology, The University of Sydney
Sydney, Australia|Sydney, Australia
Brain and Mind Centre, The University of Sydney|School of Psychology, The University of Sydney
Sydney, Australia|Sydney, Australia
Chenyu Wang
Brain and Mind Centre, The University of Sydney|Central Clinical School, The University of Sydney|Sydney Neuroimaging Analysis Centre
Sydney, Australia|Sydney, Australia|Sydney, Australia
Brain and Mind Centre, The University of Sydney|Central Clinical School, The University of Sydney|Sydney Neuroimaging Analysis Centre
Sydney, Australia|Sydney, Australia|Sydney, Australia
Introduction:
The global obesity epidemic poses significant challenges to physical and mental health. Understanding obesity and disordered eating behaviors requires identifying key risk factors in the general population that potentially contribute to disordered eating behaviours. Cognitive flexibility (CF) reflects the capacity to adapt thoughts, emotions, and behaviours to dynamic contexts. Importantly, CF has been proposed as a candidate mechanism in mitigating overeating tendency by enabling individuals to inhibit habitual responses and switch to more adaptive and flexible behavioural patterns (Uddin, 2021). Despite its potential importance, no study to our knowledge has systematically investigated the associations between CF, its neural correlates, and tendency towards overeating. This study aims to investigate whether CF is associated with overeating tendency in a healthy adult population and to explore the underlying neural substrates, focusing on five selected white matter (WM) tracts associated with CF (Perry et al., 2009), including the superior longitudinal fasciculus II (SLF II), the uncinate fasciculus (UF) in both hemispheres, and the anterior part (Genu) of the corpus callosum (CC).
Methods:
A total of 197 adults (126 females), aged 20 to 80 years, were included in this study, utilizing the diffusion MRI (dMRI) and questionnaire data from the Leipzig Study for Mind-Body-Emotion Interactions (LEMON) database (Babayan et al., 2019). CF was assessed using the Trail Making Test (TMT B-A) (Reitan, 1956), with higher values indicating poorer CF. dMRI data were processed using the standard pipeline (Glasser et al., 2013), and the corresponding WM fibre tracts were segmented with the deep learning-based method TractSeg (Wasserthal et al., 2018). Fractional anisotropy (FA) values of the selected five major WM tracts were used as indicators of WM integrity, with higher FA values reflecting greater structural integrity. The German version of the Three-Factor Eating Questionnaire (TFEQ) (Stunkard & Messick, 1985) was used to measure the overeating tendency. Spearman correlation analyses, controlling for depression and sex, were conducted to examine the associations between CF, the three TFEQ subscales and FA values of the five white matter tracts. False Discovery Rate (FDR) corrections were applied to the p-values in correlation analysis to control for multiple comparisons.
Results:
Spearman correlation analysis revealed a negative association between the TMT B-A and the Cognitive Restraint of Eating score (r = -0.18, p = 0.012) and a positive association with the Susceptibility to Hunger score (r = 0.18, p = 0.012). These findings suggest that individuals with poorer CF are less likely to show restraint when eating. On the neural level, TMT B-A showed a significant negative association with the FA values of all major white matter fibre tracts (r range: -0.32 to -0.25; all p values < 0.001). These findings suggest that individuals with poorer CF show reduced WM integrity in key tracts. Further analysis indicated that the Cognitive Restraint of Eating was negatively correlated with the FA of the anterior CC (r = -0.16, p = 0.023), as well as the SLF II in the left hemisphere (r = -0.15, p = 0.034) and the right hemisphere (r = -0.21, p = 0.003). Conversely, the Susceptibility to Hunger was positively correlated with the FA of all five selected white matter fibre tracts (r range: 0.30 to 0.41; all p values < 0.001). These results underscore the importance of CF-related white matter integrity in mitigating overeating tendency caused by diminished self-control.
·Association between FA values of five selected WM tracts and overeating tendency.
Conclusions:
Based on self-reported questionnaires, behavioural assessments, and neuroimaging data from LEMON database, we found that CF is associated with overeating tendency at both behavioural and neural levels. These findings enhance our understanding of the relationship between CF and overeating tendency and suggest that improving CF may serve as a potential intervention strategy for managing overeating.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
Keywords:
Cognition
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - cognitive flexibility
1|2Indicates the priority used for review
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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.
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Provide references using APA citation style.
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., ... & Wu-Minn HCP Consortium. (2013). The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage, 80, 105-124.
Perry, M. E., McDonald, C. R., Hagler, D. J., Gharapetian, L., Kuperman, J. M., Koyama, A. K., . . . McEvoy, L. K. (2009). White matter tracts associated with set-shifting in healthy aging. Neuropsychologia, 47(13), 2835-2842. doi:10.1016/j.neuropsychologia.2009.06.008
Reitan, R. M. (1956). Trail Making Test: Manual for administration, scoring and interpretation (Vol. 134). Bloomington: Indiana University.
Stunkard, A. J., & Messick, S. (1985). The three-factor eating questionnaire to measure dietary restraint, disinhibition and hunger. Journal of Psychosomatic Research, 29(1), 71-83. doi:10.1016/0022-3999(85)90010-8
Uddin, L. Q. (2021). Cognitive and behavioural flexibility: neural mechanisms and clinical considerations. Nature Reviews Neuroscience, 22(3), 167-179. doi:10.1038/s41583-021-00428-w
Wasserthal, J., Neher, P., & Maier-Hein, K. H. (2018). TractSeg-Fast and accurate white matter tract segmentation. NeuroImage, 183, 239-253.