Abstract
The main aim of the present study was to investigate the association between body shape concerns and electroencephalography (EEG) functional connectivity within body image network in a sample of university students (N = 68). EEG was recorded during 5 min of resting state. All participants were asked to complete self-report measures assessing certain psychopathological dimensions (i.e., body shape concerns, depression, anxiety, obsessive-compulsive symptoms). EEG analyses were conducted by means of the exact low-resolution electromagnetic tomography software (eLORETA). Our results showed that body shape concerns were positively associated with increased gamma functional connectivity between the left and right prefrontal cortex (PFC). Furthermore, our data revealed that this EEG pattern was independently associated with body shape concerns after controlling for potential socio-demographic and clinical confounding variables. This finding seems to suggest that increased EEG gamma connectivity between the left and right PFC might be a relevant neurophysiological alteration involved in the development and/or maintenance of dysfunctional concerns about one’s body.
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References
Adenzato M et al (2019) Activating attachment memories affects default mode network in a non-clinical sample with perceived dysfunctional parenting: an EEG functional connectivity study. Behav Brain Res 372:112059. https://doi.org/10.1016/j.bbr.2019.112059
Allen MS, Walter EE (2016) Personality and body image: a systematic review. Body Image 19:79–88. https://doi.org/10.1016/j.bodyim.2016.08.012
Balsamo M, Saggino A (2014) Determining a diagnostic cut-off on the teate depression inventory. Neuropsychiatr Dis Treat 10:987–995. https://doi.org/10.2147/NDT.S55706
Barberio AM et al (2019) Central body fatness is a stronger predictor of cancer risk than overall body size. Nat Commun 10:383. https://doi.org/10.1038/s41467-018-08159-w
Bliss CI, Greenwood ML, White ES (1956) A Rankit analysis of paired comparisons for measuring the effect of sprays on flavor. Biometrics 12:381–403. https://doi.org/10.2307/3001679
Blume M, Schmidt R, Hilbert A (2019) Abnormalities in the EEG power spectrum in bulimia nervosa, binge-eating disorder, and obesity: a systematic review. Eur Eat Disord Rev 27:124–136. https://doi.org/10.1002/erv.2654
Canuet L et al (2012) Resting-state network disruption and APOE genotype in Alzheimer’s disease: a lagged functional connectivity study. PLoS ONE 7:e46289. https://doi.org/10.1371/journal.pone.0046289
Cash TF (2004) Body image: past, present, and future. Body Image 1:1–5. https://doi.org/10.1016/S1740-1445(03)00011-1
Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale
Davey CG, Pujol J, Harrison BJ (2016) Mapping the self in the brain’s default mode network. Neuroimage 132:390–397. https://doi.org/10.1016/j.neuroimage.2016.02.022
Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12:43–56. doi:https://doi.org/10.1038/nrn2961
Deli E, Tozzi A, Peters JF (2017) Relationships between short and fast brain timescales. Cogn Neurodyn 11:539–552. doi:https://doi.org/10.1007/s11571-017-9450-4
Delorme A, Makeig S (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods 134:9–21. doi:https://doi.org/10.1016/j.jneumeth.2003.10.009
Dowson J, Henderson L (2001) The validity of a short version of the body shape. Quest Psychiatry Res 102:263–271. https://doi.org/10.1016/s0165-1781(01)00254-2
Fassino S, Marzola E (2018) Diagnostic classification of body image disturbances. In: Cuzzolaro M, Fassino S (eds) Body image, eating, and weight. A guide to assessment, treatment, and prevention. Springer, Cham, pp 25–37
Fehr T, Milz P (2019) The individuality index: a measure to quantify the degree of inter-individual, spatial variability in intra-cerebral brain electric and metabolic activity. Cogn Neurodyn 13:429–436. doi:https://doi.org/10.1007/s11571-019-09538-9
Fiske L, Fallon EA, Blissmer B, Redding CA (2014) Prevalence of body dissatisfaction among United States adults: review and recommendations for future research. Eat Behav 15:357–365. doi:https://doi.org/10.1016/j.eatbeh.2014.04.010
Foa EB, Huppert JD, Leiberg S, Langner R, Kichic R, Hajcak G, Salkovskis PM (2002) The obsessive-compulsive inventory: development and validation of a short version. Psychol Assess 14:485–496. https://doi.org/10.1037/1040-3590.14.4.485
Garner DM, Olmsted MP, Bohr Y, Garfinkel PE (1982) The eating attitudes test: psychometric features and clinical correlates. Psychol Med 12:871–878. doi:https://doi.org/10.1017/s0033291700049163
Gaudio S, Quattrocchi CC (2012) Neural basis of a multidimensional model of body image distortion in anorexia nervosa. Neurosci Biobehav Rev 36:1839–1847. doi:https://doi.org/10.1016/j.neubiorev.2012.05.003
Gaudio S, Wiemerslage L, Brooks SJ, Schioth HB (2016) A systematic review of resting-state functional-MRI studies in anorexia nervosa: evidence for functional connectivity impairment in cognitive control and visuospatial and body-signal integration. Neurosci Biobehav Rev 71:578–589. https://doi.org/10.1016/j.neubiorev.2016.09.032
Gaudio S, Dakanalis A, Fariello G, Riva G (2018) Neuroscience, brain imaging, and body image in eating and weight disorders. In: Cuzzolaro M, Fassino S (eds) Body image, eating, and weight. A guide to assessment, treatment, and prevention, 2nd edn. Springer, Cham, pp 97–111
Hata M et al (2016) Functional connectivity assessed by resting state EEG correlates with cognitive decline of Alzheimer’s disease—an eLORETA study. Clin Neurophysiol 127:1269–1278. https://doi.org/10.1016/j.clinph.2015.10.030
Imperatori C et al (2019) Default mode network alterations in individuals with high-trait-anxiety: an EEG functional connectivity study. J Affect Disord 246:611–618. https://doi.org/10.1016/j.jad.2018.12.071
Imperatori C et al (2020) Increased resting state triple network functional connectivity in undergraduate problematic Cannabis users: a preliminary EEG coherence study. Brain Sci. https://doi.org/10.3390/brainsci10030136
Jatoi MA, Kamel N, Malik AS, Faye I (2014) EEG based brain source localization comparison of sLORETA and eLORETA. Australas Phys Eng Sci Med 37:713–721. doi:https://doi.org/10.1007/s13246-014-0308-3
Jauregui-Lobera I (2012) Electroencephalography in eating disorders. Neuropsychiatr Dis Treat 8:1–11. doi:https://doi.org/10.2147/NDT.S27302
Li Y, Cao D, Wei L, Tang Y, Wang J (2015) Abnormal functional connectivity of EEG gamma band in patients with depression during emotional face processing. Clin Neurophysiol 126:2078–2089. doi:https://doi.org/10.1016/j.clinph.2014.12.026
Nichols TE, Holmes AP (2002) Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp 15:1–25. doi:https://doi.org/10.1002/hbm.1058
Pascual-Marqui RD et al (2011) Assessing interactions in the brain with exact low-resolution electromagnetic tomography. Philos Trans A Math Phys Eng Sci 369:3768–3784. doi:https://doi.org/10.1098/rsta.2011.0081
Radwan H et al (2019) Body mass index perception, body image dissatisfaction and their relations with weight-related behaviors among University Students. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph16091541
Ree MJ, MacLeod C, French D, Locke V (2000) State-trait inventory for cognitive and somatic anxiety (STICSA). The University of Western Australia, Australia
Soloman SR, Sawilowsky SS (2009) Impact of rank-based normalizing transformations on the accuracy of test scores. J Mod Appl Stat Methods 8:448–462. doi:https://doi.org/10.22237/jmasm/1257034080
Whitton AE, Deccy S, Ironside ML, Kumar P, Beltzer M, Pizzagalli DA (2018) Electroencephalography source functional connectivity reveals abnormal high-frequency communication among large-scale functional networks in depression. Biol Psychiatry Cogn Neurosci Neuroimaging 3:50–58. https://doi.org/10.1016/j.bpsc.2017.07.001
Yao D et al (2020) Bacomics: a comprehensive cross area originating in the studies of various brain–apparatus conversations. Cogn Neurodyn. https://doi.org/10.1007/s11571-020-09577-7
Zhang X, Lei X, Wu T, Jiang T (2014) A review of EEG and MEG for brainnetome research. Cogn Neurodyn 8:87–98. doi:https://doi.org/10.1007/s11571-013-9274-9
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Imperatori, C., Panno, A., Giacchini, M. et al. Electroencephalographic correlates of body shape concerns: an eLORETA functional connectivity study. Cogn Neurodyn 14, 723–729 (2020). https://doi.org/10.1007/s11571-020-09618-1
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DOI: https://doi.org/10.1007/s11571-020-09618-1