Alterations in intrinsic neural timescale across waist circumference
Poster No:
1621
Submission Type:
Abstract Submission
Authors:
Seoah Lee1, Bo-yong Park2,3
Institutions:
1Department of Mathematics, Inha University, Incheon, Republic of Korea, 2Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea, 3Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
First Author:
Co-Author:
Bo-yong Park
Department of Brain and Cognitive Engineering, Korea University|Center for Neuroscience Imaging Research, Institute for Basic Science
Seoul, Republic of Korea|Suwon, Republic of Korea
Department of Brain and Cognitive Engineering, Korea University|Center for Neuroscience Imaging Research, Institute for Basic Science
Seoul, Republic of Korea|Suwon, Republic of Korea
Introduction:
Intrinsic neural timescale (INT), measured via the autocorrelation of neural activity, reflects how long a brain region retains and integrates information (Murray et al., 2014; Ito et al., 2020; Raut et al., 2020). Previous studies demonstrated a hierarchical organization of INT, where the regions involved in sensory processing exhibit shorter INTs, while those supporting higher-order cognitive functions, such as planning and memory, have longer INTs (Murray et al., 2014; Ito et al., 2020; Raut et al., 2020). Given that obesity has been identified as a phenotype associated with hierarchical disruptions in brain structure and function (Park et al., 2021; Lee et al., 2022), we hypothesized that obesity may be linked to variations in INT across the brain. In this study, we investigated changes in INT in relation to an obesity phenotype, specially waist circumference (WC).
Methods:
We obtained functional magnetic resonance imaging (fMRI) data of 302 participants (mean age ± standard deviation = 37.63 ± 13.77 years; 193 females) from the enhanced Nathan Kline Institute Rockland Sample database (Nooner et al., 2012). The data were preprocessed using the Fusion of Neuroimaging Preprocessing (FuNP) pipeline (B. Y. Park et al., 2019). INT was calculated for 300 cortical regions parcellated using the Schaefer atlas (Schaefer et al., 2018) by fitting the autocorrelation function to a nonlinear exponential function with an offset. The relationship between INT and WC was assessed using least absolute shrinkage and selection operator (LASSO) regression, adjusting for age and sex. The optimal regularization parameter was determined using five-fold cross-validation. The mean INT values of regions with non-zero coefficients were then correlated with WC. Additionally, we classified individuals with abdominal obesity (WC > 94 cm for males and WC > 80 cm for females) from healthy weight controls (WC ≤ 94 cm for males and WC ≤ 80 cm for females) (WHO, 2011) using logistic regression to evaluate how well INT describes group differences.
Results:
The calculated INT values ranged from approximately 2.58 to 10.66 ms across the brain (Fig. 1A). Shorter INT values were observed in sensory/motor regions, and longer INT values were identified in the frontal and temporal regions. Associations between INT and WC revealed that individuals with larger WC exhibited shorter INT in the frontotemporal, limbic and reward systems (Fig. 1B). The correlation between the mean INT of the identified regions and WC was -0.27 (p < 0.001). Furthermore, INT of the identified regions distinguished individuals with abdominal obesity from healthy weight controls, achieving the AUC value of 0.71. These findings indicate that INT may serve as a potential imaging marker for abdominal obesity.
Conclusions:
In this study, we investigated the relationship between INT and the obesity phenotype, observing decreased neural processing times in higher-order cognitive control and reward-related brain regions. These regions typically require sufficient time for effective information processing. However, our findings suggest that in individuals with abdominal obesity, neurons in these regions may fail to retain and integrate information. Our findings provide insights into the neural processing mechanisms underlying obesity and may contribute to a deeper understanding of the brain-behavior relationship in this population.
Funding : This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks; RS-2021-II212068, Artificial Intelligence Innovation Hub), and Institute for Basic Science (IBS-R015-D1).
Funding : This work was supported by the Institute for Information and Communications Technology Planning and Evaluation (IITP) funded by the Korea Government (MSIT) (No. 2022-0-00448/RS-2022-II220448, Deep Total Recall: Continual Learning for Human-Like Recall of Artificial Neural Networks; RS-2021-II212068, Artificial Intelligence Innovation Hub), and Institute for Basic Science (IBS-R015-D1).
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Modeling and Analysis Methods:
Other Methods 1
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Other - Intrinsic neural timescale; Obesity
1|2Indicates the priority used for review
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2. Ito, T. (2020). A cortical hierarchy of localized and distributed processes revealed via dissociation of task activations, connectivity changes, and intrinsic timescales. NeuroImage, 221, 117141.
3. Raut, R. V. (2020). Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proceedings of the National Academy of Sciences of the United States of America, 117(34), 20890–20897.
4. Park, B. Y. (2021). Inter-individual body mass variations relate to fractionated functional brain hierarchies. Communications Biology, 4(1), 1–12.
5. Lee, H. (2022). Disrupted stepwise functional brain organization in overweight individuals. Communications Biology, 5(1), 1–9.
6. Nooner, K. B. (2012). The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry. Frontiers in Neuroscience, 6(OCT).
7. Park, B. Y. (2019). FuNP (fusion of neuroimaging preprocessing) pipelines: A fully automated preprocessing software for functional magnetic resonance imaging. Frontiers in Neuroinformatics, 13, 416949.
8. Schaefer, A. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.
9. Yeo, B. T. T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125.
10. World Health Organization (WHO). (2011). Waist circumference and waist–hip ratio. WHO Expert, 64(1), 2–5.