超重/肥胖个体工作记忆的神经机制及干预

doi:10.3724/SP.J.1042.2023.01775

摘要/Abstract

摘要：

全球超重/肥胖人群增长迅猛, 1997年世界卫生组织将肥胖认定为全球性流行病。目前, 中国成年人的超重/肥胖发生率已超过50%, 不健康的饮食行为占肥胖成因的70%。本项目拟从食物特异工作记忆切入, 探明超重/肥胖个体食物特异工作记忆的神经机制, 以及与一般工作记忆神经机制的差异。再采用前瞻性的研究设计, 考察食物特异工作记忆与超重/肥胖发展的渐变关系, 探索食物特异工作记忆及其神经活动对个体饮食管理和体质变化的预测作用。最后, 采用食物特异抑制控制训练, 提升超重/肥胖个体的食物特异工作记忆能力, 塑造健康饮食行为。本项目旨在探索塑造健康饮食行为的安全及有效的方法, 为超重/肥胖的预防和干预提供理论和实践建议, 具有现实性、前沿性和前瞻性。

关键词: 工作记忆, 认知控制, 超重/肥胖, 认知神经机制, 干预

Abstract:

In 1997, the World Health Organization recognized obesity as a global epidemic. In China, the prevalence of overweight/obesity among adults has surpassed 50%, with unhealthy dietary behaviors accounting for 70% of the causes. Working memory has been shown to play a protective role in maintaining long-term healthy dietary goals by diverting attention from tempting stimuli. Therefore, this research project aims to investigate the role of food-specific working memory in individuals with overweight/obesity through cross-sectional, prospective, and intervention studies. The research will explore temporal dynamics, neural oscillations, brain spatial activation, and real-life implications. The project's objectives are as follows: (1) to explore the cognitive and neural mechanisms involved in food-specific working memory updating in overweight/obese individuals; (2) to examine the predictive function of food-specific working memory updating and related neural activity on an individual’s dietary management and weight changes; (3) to investigate the effectiveness of food-specific inhibitory control training in enhancing food-specific working memory updating and promoting healthy dietary habits in individuals with overweight/obesity.

Study 1 will employ electroencephalography (EEG) techniques to investigate the electrophysiological activity underlying working memory updating in overweight/obese individuals. This study will use a 2-back task with general and food-specific stimuli. The study will examine the temporal characteristics of brain activity associated with general and food-specific working memory updating in overweight/obese individuals and investigate whether there are similar behavioral and neural patterns between general and food-specific working memory updating. It is hypothesized that overweight/obese individuals will exhibit significantly different performance in the 2-back task compared to normal-weight individuals, and the neural correlates may involve changes in N2 amplitude, P3 amplitude, theta and alpha power, among others. Additionally, due to the rewarding effects of food, general and food-specific working memory updating in overweight/obese individuals may exhibit different neural patterns.

Study 2 will focus on the relationship between food-specific working memory updating and related brain activity, and the development of overweight/obesity using functional magnetic resonance imaging (fMRI). This study will consist of two experiments—a cross-sectional study design and a prospective study design. The study will explore the predictive role of food-specific working memory updating and related brain activity on dietary management and changes in body weight in the overweight/obesity population. The study will first utilize food-specific 1-back tasks with inhibitory control and then collect data through follow-up surveys and body composition measurements. It is hypothesized that overweight/obese individuals will display poorer performance in the working memory task and exhibit less brain activation in control-related brain regions, as well as greater activation in reward-related brain regions, compared to normal-weight individuals during the task.

Study 3 aims to explore effective interventions for overweight/obesity by employing food inhibition control training combined with fMRI techniques. The study will include a general and a food-specific inhibition control training delivered through general or food-specific go/no-go tests. Both trainings will be investigated, with the hypothesis that both types of training can improve food-specific working memory updating performance, and both trainings can enhance the activity in control-related brain regions involved in food-specific working memory in overweight/obese individuals, but food-specific training will yield better results.

In summary, this project delves into the behavioral and neural mechanisms of working memory in individuals with overweight/obesity. By investigating the cognitive processing, spatial activation patterns, and the interplay between food-specific working memory and overweight/obesity, the research aims to provide reliable evidence and a comprehensive understanding of the cognitive and neural mechanisms in this population. The project will also examine the interdependent relationship between food-specific working memory and related brain activity, and the development of overweight/obesity, with the goal of obtaining a wholistic view of the underpinnings between working memory updating and overweight/obesity and providing evidence for the establishment of a more complete neurocognitive model. Furthermore, the project will employ inhibition control training as an intervention for overweight/obesity, laying a practical foundation for effective solutions to obesity-related issues and facilitate the innovative translation of basic research findings.

Key words: working memory, cognitive control, overweight/obesity, cognitive neural mechanism, intervention

中图分类号:

B845

刘永, 陈红. (2023). 超重/肥胖个体工作记忆的神经机制及干预. 心理科学进展 , 31(10), 1775-1784.

LIU Yong, CHEN Hong. (2023). Neural mechanism of food-related working memory in individuals with overweight/obesity and related intervention. Advances in Psychological Science, 31(10), 1775-1784.

图/表 1

参考文献 58

[1]	库逸轩. (2019). 工作记忆的认知神经机制. 生理学报, 71(1), 173-185.
[2]	刘豫, 陈红, 李书慧, 罗念. (2017). 在线抑制控制训练对失败的限制性饮食者不健康食物选择的改善. 心理学报, 49(2), 219.
[3]	Allom, V., & Mullan, B. (2014). Individual differences in executive function predict distinct eating behaviours. Appetite, 80, 123-130. https://doi.org/10.1016/j.appet.2014.05.007 doi: 10.1016/j.appet.2014.05.007 URL pmid: 24845785
[4]	Barrett, L. F., Tugade, M. M., & Engle, R. W. (2004). Individual differences in working memory capacity and dual-process theories of the mind. Psychological Bulletin, 130(4), 553-573. https://doi.org/10.1037/0033-2909.130.4.553 doi: 10.1037/0033-2909.130.4.553 URL pmid: 15250813
[5]	Batterink, L., Yokum, S., & Stice, E. (2010). Body mass correlates inversely with inhibitory control in response to food among adolescent girls: An fmri study. Neuroimage, 52(4), 1696-1703. https://doi.org/10.1016/j.neuroimage.2010.05.059 doi: 10.1016/j.neuroimage.2010.05.059 URL pmid: 20510377
[6]	Bonnefond, M., & Jensen, O. (2012). Alpha oscillations serve to protect working memory maintenance against anticipated distracters. Current Biology, 22(20), 1969-1974. https://doi.org/10.1016/j.cub.2012.08.029 doi: 10.1016/j.cub.2012.08.029 URL pmid: 23041197
[7]	Bruce, A. S., Lepping, R. J., Bruce, J. M., Cherry, J. B. C., Martin, L. E., Davis, A. M., ... Savage, C. R. (2013). Brain responses to food logos in obese and healthy weight children. The Journal of Pediatrics, 162(4), 759-764. https://doi.org/10.1016/j.jpeds.2012.10.003 doi: 10.1016/j.jpeds.2012.10.003 URL pmid: 23211928
[8]	Cohen, J. D., Perlstein, W. M., Braver, T. S., Nystrom, L. E., Noll, D. C., Jonides, J., & Smith, E. E. (1997). Temporal dynamics of brain activation during a working memory task. Nature, 386(6625), 604-608. https://doi.org/10.1038/386604a0 doi: 10.1038/386604a0 URL
[9]	Delgado-Rodríguez, R., Versace, F., Hernández-Rivero, I., Guerra, P., Fernández-Santaella, M. C., & Miccoli, L. (2022). Food addiction symptoms are related to neuroaffective responses to preferred binge food and erotic cues. Appetite, 168, 105687. https://doi.org/10.1016/j.appet.2021.105687 doi: 10.1016/j.appet.2021.105687 URL
[10]	Diamond, A. (2013). Executive functions. Annual Review of Psychology, 64, 135-168. https://doi.org/10.1146/annurev-psych-113011-143750 doi: 10.1146/annurev-psych-113011-143750 URL pmid: 23020641
[11]	Dohle, S., Diel, K., & Hofmann, W. (2018). Executive functions and the self-regulation of eating behavior: A review. Appetite, 124, 4-9. https://doi.org/10.1016/j.appet.2017.05.041 doi: S0195-6663(17)30160-5 URL pmid: 28551113
[12]	Engle, R. W. (2018). Working memory and executive attention: A revisit. Perspectives on Psychological Science, 13(2), 190-193. https://doi.org/10.1177/1745691617720478 doi: 10.1177/1745691617720478 URL pmid: 29592654
[13]	Fu, Y., Zhou, Y., Zhou, J., Shen, M., & Chen, H. (2021). More attention with less working memory: The active inhibition of attended but outdated information. Science Advances, 7(47), eabj4985. https://doi.org/10.1126/sciadv.abj4985 doi: 10.1126/sciadv.abj4985 URL
[14]	Gazzaley, A., Cooney, J. W., Rissman, J., & D'esposito, M. (2005). Top-down suppression deficit underlies working memory impairment in normal aging. Nature Neuroscience, 8(10), 1298-1300. https://doi.org/10.1038/nn1543 doi: 10.1038/nn1543 URL pmid: 16158065
[15]	Gevins, A., Smith, M. E., McEvoy, L., & Yu, D. (1997). High-resolution eeg mapping of cortical activation related to working memory: Effects of task difficulty, type of processing, and practice. Cerebral Cortex, 7(4), 374-385. https://doi.org/10.1093/cercor/7.4.374 URL pmid: 9177767
[16]	Goldschmidt, A. B., O'Brien, S., Lavender, J. M., Pearson, C. M., Le, G. D., & Hunter, S. J. (2017). Executive functioning in a racially diverse sample of children who are overweight and at risk for eating disorders. Appetite, 124, 43-49. https://doi.org/10.1016/j.appet.2017.03.010 doi: 10.1016/j.appet.2017.03.010 URL
[17]	Hasher, L., & Zacks, R. T. (1988). Working memory, comprehension, and aging: A review and a new view. Psychology Of Learning And Motivation, 22, 193-225. https://doi.org/10.1016/S0079-7421(08)60041-9
[18]	Hofmann, W., Gschwendner, T., Friese, M., Wiers, R. W., & Schmitt, M. (2008). Working memory capacity and self-regulatory behavior: Toward an individual differences perspective on behavior determination by automatic versus controlled processes. Journal of Personality and Social Psychology, 95(4), 962-977. https://doi.org/10.1037/a0012705 doi: 10.1037/a0012705 URL pmid: 18808271
[19]	Hofmann, W., Schmeichel, B. J., & Baddeley, A. D. (2012). Executive functions and self-regulation. Trends in Cognitive Sciences, 16(3), 174-180. https://doi.org/10.1016/j.tics.2012.01.006 doi: 10.1016/j.tics.2012.01.006 URL pmid: 22336729
[20]	Houben, K., & Jansen, A. (2015). Chocolate equals stop. Chocolate-specific inhibition training reduces chocolate intake and go associations with chocolate. Appetite, 87, 318-323. https://doi.org/10.1016/j.appet.2015.01.005 doi: 10.1016/j.appet.2015.01.005 URL pmid: 25596041
[21]	Janssen, L. K., Duif, I., van Loon, I., Wegman, J., de Vries, J. H. M., Cools, R., & Aarts, E. (2017). Loss of lateral prefrontal cortex control in food-directed attention and goal-directed food choice in obesity. Neuroimage, 146, 148-156. https://doi.org/10.1016/j.neuroimage.2016.11.015 doi: S1053-8119(16)30639-5 URL pmid: 27845255
[22]	Jensen, O., Gelfand, J., Kounios, J., & Lisman, J. E. (2002). Oscillations in the alpha band (9-12 hz) increase with memory load during retention in a short-term memory task. Cerebral Cortex, 12(8), 877-882. https://doi.org/10.1093/cercor/12.8.877 doi: 10.1093/cercor/12.8.877 URL pmid: 12122036
[23]	Kaisari, P., Kumar, S., Hattersley, J., Dourish, C. T., Rotshtein, P., & Higgs, S. (2019). Top-down guidance of attention to food cues is enhanced in individuals with overweight/obesity and predicts change in weight at one-year follow up. International Journal of Obesity, 43(9), 1849-1858. https://doi.org/10.1038/s41366-018-0246-3 doi: 10.1038/s41366-018-0246-3 URL pmid: 30464229
[24]	Killgore, W., Weber, M., Schwab, Z., Kipman, M., DelDonno, S., Webb, C., & Rauch, S. (2013). Cortico-limbic responsiveness to high-calorie food images predicts weight status among women. International Journal of Obesity, 37(11), 1435-1442. https://doi.org/10.1038/ijo.2013.26 doi: 10.1038/ijo.2013.26 URL pmid: 23459322
[25]	Kong, F., Zhang, Y., & Chen, H. (2015). Inhibition ability of food cues between successful and unsuccessful restrained eaters: A two-choice oddball task. PLoS One, 10(7), e0133942. https://doi.org/10.1371/journal.pone.0133942 doi: 10.1371/journal.pone.0133942 URL
[26]	Kopell, N., Whittington, M. A., & Kramer, M. A. (2011). Neuronal assembly dynamics in the beta1 frequency range permits short-term memory. Proceedings of the National Academy of Sciences, 108(9), 3779-3784. https://doi.org/10.1073/pnas.1019676108 doi: 10.1073/pnas.1019676108 URL
[27]	Lamichhane, B., Westbrook, A., Cole, M. W., & Braver, T. S. (2020). Exploring brain-behavior relationships in the n-back task. Neuroimage, 212, 116683. https://doi.org/10.1016/j.neuroimage.2020.116683 doi: 10.1016/j.neuroimage.2020.116683 URL
[28]	Li, S., Cai, Y., Liu, J., Li, D., Feng, Z., Chen, C., & Xue, G. (2017). Dissociated roles of the parietal and frontal cortices in the scope and control of attention during visual working memory. Neuroimage, 149, 210-219. https://doi.org/10.1016/j.neuroimage.2017.01.061 doi: S1053-8119(17)30085-X URL pmid: 28131893
[29]	Liu, Y., Gao, X., Zhao, J., Zhang, L., & Chen, H. (2020). Neurocognitive correlates of food-related response inhibition in overweight/obese adults. Brain Topography, 33(1), 101-111. https://doi.org/10.1007/s10548-019-00730-y doi: 10.1007/s10548-019-00730-y URL pmid: 31564028
[30]	Liu, Y., Quan, H., Song, S., Zhang, X., & Chen, H. (2019). Decreased conflict control in overweight Chinese females: Behavioral and event-related potentials evidence. Nutrients, 11(7), 1450. https://doi.org/10.3390/nu11071450 doi: 10.3390/nu11071450 URL
[31]	Liu, Y., Zhao, J., Zhang, X., Gao, X., & Chen, H. (2019). Overweight adults are more impulsive than normal weight adults: Evidence from erps during a chocolate-related delayed discounting task. Neuropsychologia, 133, 107181. https://doi.org/10.1016/j.neuropsychologia.2019.107181 doi: 10.1016/j.neuropsychologia.2019.107181 URL
[32]	Loeber, S., Grosshans, M., Korucuoglu, O., Vollmert, C., Vollstädt-klein, S., Schneider, S., ... Kiefer, F. (2012). Impairment of inhibitory control in response to food-associated cues and attentional bias of obese participants and normal-weight controls. International Journal of Obesity, 36(10), 1334-1339. http://doi.org/10.1038/ijo.2011.184 doi: 10.1038/ijo.2011.184 URL pmid: 21986703
[33]	Lopez, R. B., Chen, P.-H. A., Huckins, J. F., Hofmann, W., Kelley, W. M., & Heatherton, T. F. (2017). A balance of activity in brain control and reward systems predicts self-regulatory outcomes. Social Cognitive and Affective Neuroscience, 12(5), 832-838. https://doi.org/10.1093/scan/nsx004 doi: 10.1093/scan/nsx004 URL pmid: 28158874
[34]	Lopez, R. B., Hofmann, W., Wagner, D. D., Kelley, W. M., & Heatherton, T. F. (2014). Neural predictors of giving in to temptation in daily life. Psychological Science, 25(7), 1337-1344. https://doi.org/10.1177/0956797614531492 doi: 10.1177/0956797614531492 URL pmid: 24789842
[35]	Lopez, R. B., Milyavskaya, M., Hofmann, W., & Heatherton, T. F. (2016). Motivational and neural correlates of self-control of eating: A combined neuroimaging and experience sampling study in dieting female college students. Appetite, 103, 192-199. https://doi.org/10.1016/j.appet.2016.03.027 doi: S0195-6663(16)30121-0 URL pmid: 27058281
[36]	Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L., & Miller, E. K. (2018). Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nature Communication, 9(1), 394. https://doi.org/10.1038/s41467-017-02791-8 doi: 10.1038/s41467-017-02791-8 URL
[37]	Meng, X., Huang, D., Ao, H., Wang, X., & Gao, X. (2020). Food cue recruits increased reward processing and decreased inhibitory control processing in the obese/overweight: An activation likelihood estimation meta-analysis of fmri studies. Obesity Research & Clinical Practice, 14(2), 127-135. https://10.1016/j.orcp.2020.02.004
[38]	Meule, A., Kübler, A., & Blechert, J. (2013). Time course of electrocortical food-cue responses during cognitive regulation of craving. Frontiers in Psychology, 4, 669. https://10.3389/fpsyg.2013.00669 doi: 10.3389/fpsyg.2013.00669 URL pmid: 24098290
[39]	Miller, E. K., & Cohen, J. D. (2001). An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24(1), 167-202. https://10.1146/annurev.neuro.24.1.167 doi: 10.1146/neuro.2001.24.issue-1 URL
[40]	Miller, E. K., Lundqvist, M., & Bastos, A. M. (2018). Working memory 2.0. Neuron, 100(2), 463-475. https://doi.org/10.1016/j.neuron.2018.09.023 doi: S0896-6273(18)30825-0 URL pmid: 30359609
[41]	Murdaugh, D. L., Cox, J. E., Cook III, E. W., & Weller, R. E. (2012). fMRI reactivity to high-calorie food pictures predicts short-and long-term outcome in a weight-loss program. Neuroimage, 59(3), 2709-2721. https://doi.org/10.1016/j.neuroimage.2011.10.071 URL pmid: 22332246
[42]	Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C., ... Gakidou, E. (2014). Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: A systematic analysis for the global burden of disease study 2013. The Lancet, 384(9945), 766-781. https://doi.org/10.1016/S0140-6736(14)60460-8 doi: 10.1016/S0140-6736(14)60460-8 URL
[43]	Perlstein, W. M., Dixit, N. K., Carter, C. S., Noll, D. C., & Cohen, J. D. (2003). Prefrontal cortex dysfunction mediates deficits in working memory and prepotent responding in schizophrenia. Biological Psychiatry, 53(1), 25-38. https://doi.org/10.1016/S0006-3223(02)01675-X doi: 10.1016/s0006-3223(02)01675-x URL pmid: 12513942
[44]	Raghavachari, S., Kahana, M. J., Rizzuto, D. S., Caplan, J. B., Kirschen, M. P., Bourgeois, B., ... Lisman, J. E. (2001). Gating of human theta oscillations by a working memory task. Journal of Neuroscience, 21(9), 3175-3183. https://doi.org/10.1523/jneurosci.21-09-03175.2001 URL pmid: 11312302
[45]	Rutters, F., Kumar, S., Higgs, S., & Humphreys, G. W. (2015). Electrophysiological evidence for enhanced representation of food stimuli in working memory. Experimental Brain Research, 233(2), 519-528. https://doi.org/10.1007/s00221-014-4132-5 doi: 10.1007/s00221-014-4132-5 URL pmid: 25354971
[46]	Salazar, R., Dotson, N., Bressler, S., & Gray, C. (2012). Content-specific fronto-parietal synchronization during visual working memory. Science, 338(6110), 1097-1100. https://doi.org/10.1126/science.1224000 doi: 10.1126/science.1224000 URL pmid: 23118014
[47]	Spitzer, B., Fleck, S., & Blankenburg, F. (2014). Parametric alpha-and beta-band signatures of supramodal numerosity information in human working memory. Journal of Neuroscience, 34(12), 4293-4302. https://doi.org/10.1523/jneurosci.4580-13.2014 doi: 10.1523/JNEUROSCI.4580-13.2014 URL
[48]	Stice, E., & Burger, K. (2019). Neural vulnerability factors for obesity. Clinical Psychology Review, 68, 38-53. https://doi.org/10.1016/j.cpr.2018.12.002 doi: S0272-7358(18)30162-4 URL pmid: 30587407
[49]	Stingl, K. T., Kullmann, S., Ketterer, C., Heni, M., Häring, H.-U., Fritsche, A., & Preissl, H. (2012). Neuronal correlates of reduced memory performance in overweight subjects. NeuroImage, 60(1), 362-369. /https://doi.org/10.1016/j.neuroimage.2011.12.012 doi: 10.1016/j.neuroimage.2011.12.012 URL pmid: 22197786
[50]	Vogel, E. K., McCollough, A. W., & Machizawa, M. G. (2005). Neural measures reveal individual differences in controlling access to working memory. Nature, 438(7067), 500-503. https://doi.org/10.1038/nature04171 doi: 10.1038/nature04171 URL
[51]	Whitelock, V., Nouwen, A., van den Akker, O., & Higgs, S. (2018). The role of working memory sub-components in food choice and dieting success. Appetite, 124, 24-32. https://doi.org/10.1016/j.appet.2017.05.043 doi: S0195-6663(17)30039-9 URL pmid: 28554850
[52]	Wu, X., Nussbaum, M. A., & Madigan, M. L. (2016). Executive function and measures of fall risk among people with obesity. Perceptual And Motor Skills, 122(3), 825-839. https://doi.org/10.1177/0031512516646158 doi: 10.1177/0031512516646158 URL pmid: 27170627
[53]	Yang, Y., Shields, G. S., Guo, C., & Liu, Y. (2018). Executive function performance in obesity and overweight individuals: A meta-analysis and review. Neuroscience & Biobehavioral Reviews, 84, 225-244. https://doi.org/10.1016/j.neubiorev.2017.11.020 doi: 10.1016/j.neubiorev.2017.11.020 URL
[54]	Yang, Y., Shields, G. S., Wu, Q., Liu, Y., Chen, H., & Guo, C. (2019). Cognitive training on eating behaviour and weight loss: A meta-analysis and systematic review. Obesity Reviews, 20(11), 1628-1641. https://doi.org/10.1111/obr.12916 doi: 10.1111/obr.12916 URL pmid: 31353774
[55]	Yau, P. L., Kang, E. H., Javier, D. C., & Convit, A. (2014). Preliminary evidence of cognitive and brain abnormalities in uncomplicated adolescent obesity. Obesity, 22(8), 1865-1871. https://doi.org/10.1002/oby.20801 doi: 10.1002/oby.20801 URL pmid: 24891029
[56]	Yokum, S., Ng, J., & Stice, E. (2011). Attentional bias to food images associated with elevated weight and future weight gain: An fMRI study. Obesity, 19(9), 1775-1783. https://doi.org/10.1038/oby.2011.168 doi: 10.1038/oby.2011.168 URL pmid: 21681221
[57]	Zacks, T. R., & Hasher, L. (2006). Aging and long-term memory:Deﬁcits are not inevitable. In E. Bialystok & F. I. M. Craik (Eds.), Lifespan cognition: Mechanisms of change (pp. 162-177). Oxford Academic. https://doi.org/ 10.1093/acprof:oso/9780195169539.003.0011
[58]	Zhao, J., Long, Z., Li, Y., Qin, Y., & Liu, Y. (2022). Alteration of regional heterogeneity and functional connectivity for obese undergraduates: Evidence from resting-state fMRI. Brain Imaging and Behavior, 16(2), 627-636. https://doi.org/10.1007/s11682-021-00542-4 doi: 10.1007/s11682-021-00542-4 URL