Hostname: page-component-848d4c4894-wzw2p Total loading time: 0 Render date: 2024-05-14T14:23:39.757Z Has data issue: false hasContentIssue false
  • English
  • Français

Connectome-based prediction of eating disorder-associated symptomatology

Published online by Cambridge University Press:  30 September 2022

Ximei Chen ,
Debo Dong ,
Feng Zhou ,
Xiao Gao ,
Yong Liu ,
Junjie Wang ,
Jingmin Qin ,
Yun Tian ,
Mingyue Xiao  and
Xiaofei Xu
...Show all authors
Ximei Chen
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Debo Dong
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Feng Zhou
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Xiao Gao
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Yong Liu
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Junjie Wang
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Jingmin Qin
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Yun Tian
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Mingyue Xiao
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Xiaofei Xu
Affiliation:
School of Computing Technologies, RMIT University, Melbourne, Australia
Wei Li
Affiliation:
Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
Jiang Qiu
Affiliation:
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
Tingyong Feng
Affiliation:
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
Qinghua He
Affiliation:
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
Xu Lei
Affiliation:
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
Hong Chen*
Affiliation:
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
*
Author for correspondence: Hong Chen, E-mail: chenhg@swu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

Methods

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

Results

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

Conclusions

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Keywords

Body shape/weight concernsconnectome-based predictive modelingeating disorder symptomsresting-state functional connectivity
Type
Original Article
Information
Psychological Medicine , Volume 53 , Issue 12 , September 2023 , pp. 5786 - 5799
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

These authors contributed equally to this work.

References

Anderson, M. L. (2014). After phrenology: Neural reuse and the interactive brain. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Badoud, D., & Tsakiris, M. (2017). From the body's viscera to the body's image: Is there a link between interoception and body image concerns? Neuroscience and Biobehavioral Reviews, 77, 237–246. doi:10.1016/j.neubiorev.2017.03.017CrossRefGoogle Scholar
Barona, M., Brown, M., Clark, C., Frangou, S., White, T., & Micali, N. (2019). White matter alterations in anorexia nervosa: Evidence from a voxel-based meta-analysis. Neuroscience and Biobehavioral Reviews, 100, 285–295. doi:10.1016/j.neubiorev.2019.03.002CrossRefGoogle ScholarPubMed
Bartholdy, S., O'Daly, O. G., Campbell, I. C., Banaschewski, T., Barker, G., & Bokde, A., … IMAGEN Consortium. (2019). Neural correlates of failed inhibitory control as an early marker of disordered eating in adolescents. Biological Psychiatry, 85(11), 956–965. doi:10.1016/j.biopsych.2019.01.027CrossRefGoogle ScholarPubMed
Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., … Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences of the United States of America, 115(5), 1087–1092. doi:10.1073/pnas.1713532115CrossRefGoogle ScholarPubMed
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component-based noise correction method (CompCor) for BOLD and perfusion-based fMRI. NeuroImage, 37(1), 90–101. doi:10.1016/j.neuroimage.2007.04.042CrossRefGoogle ScholarPubMed
Belkin, M., Hsu, D. J., & Mitra, P. (2018). Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate. In Advances in neural information processing systems (pp. 2300–2311). doi:10.48550/arXiv.1806.05161, https://www.researchgate.net/publication/325754503_Overfitting_or_perfect_fitting_Risk_bounds_for_classification_and_regression_rules_that_interpolateGoogle Scholar
Boehm, I., Geisler, D., King, J. A., Ritschel, F., Seidel, M., Deza Araujo, Y., … Ehrlich, S. (2014). Increased resting state functional connectivity in the fronto-parietal and default mode network in anorexia nervosa. Frontiers in Behavioral Neuroscience, 8, 346. doi:10.3389/fnbeh.2014.00346CrossRefGoogle ScholarPubMed
Cai, H., Chen, J., Liu, S., Zhu, J., & Yu, Y. (2020). Brain functional connectome-based prediction of individual decision impulsivity. Cortex, 125, 288–298. doi:10.1016/j.cortex.2020.01.022CrossRefGoogle ScholarPubMed
Cash, T. F. (2004). Body image: Past, present, and future. Body Image, 1(1), 1–5. doi:10.1016/S1740-1445(03)00011-1CrossRefGoogle ScholarPubMed
Chen, H., & Jackson, T. (2008). Prevalence and sociodemographic correlates of eating disorder endorsements among adolescents and young adults from China. European Eating Disorders Review, 16(5), 375–385. doi:10.1002/erv.837CrossRefGoogle Scholar
Chen, X., Gao, X., Qin, J., Wang, C., Xiao, M., Tian, Y., … Chen, H. (2021). Resting-state functional network connectivity underlying eating disorder symptoms in healthy young adults. NeuroImage: Clinical, 30, 102671. doi:10.1016/j.nicl.2021.102671CrossRefGoogle ScholarPubMed
Cuthbert, B. N. (2015). Research domain criteria: Toward future psychiatric nosologies. Dialogues in Clinical Neuroscience, 17(1), 89–97. doi:10.31887/DCNS.2015.17.1/bcuthbertCrossRefGoogle ScholarPubMed
Domakonda, M. J., He, X., Lee, S., Cyr, M., & Marsh, R. (2019). Increased functional connectivity between ventral attention and default mode networks in adolescents with bulimia nervosa. Journal of the American Academy of Child and Adolescent Psychiatry, 58(2), 232–241. doi:10.1016/j.jaac.2018.09.433CrossRefGoogle ScholarPubMed
Duarte, C., Pinto-Gouveia, J., & Ferreira, C. (2015). Expanding binge eating assessment: Validity and screening value of the binge eating scale in women from the general population. Eating Behaviors, 18, 41–47. doi:10.1016/j.eatbeh.2015.03.007CrossRefGoogle ScholarPubMed
Favaro, A., Santonastaso, P., Manara, R., Bosello, R., Bommarito, G., Tenconi, E., & Di Salle, F. (2012). Disruption of visuospatial and somatosensory functional connectivity in anorexia nervosa. Biological Psychiatry, 72(10), 864–870. doi:10.1016/j.biopsych.2012.04.025CrossRefGoogle ScholarPubMed
Feng, C., Wang, L., Li, T., & Xu, P. (2019). Connectome-based individualized prediction of loneliness. Social Cognitive and Affective Neuroscience, 14(4), 353–365. doi:10.1093/scan/nsz020CrossRefGoogle ScholarPubMed
Feng, C., Yuan, J., Geng, H., Gu, R., Zhou, H., Wu, X., & Luo, Y. (2018). Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity. Human Brain Mapping, 39(9), 3701–3712. doi:10.1002/hbm.24205CrossRefGoogle ScholarPubMed
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., … Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. doi:10.1038/nn.4135CrossRefGoogle ScholarPubMed
Forcano, L., Mata, F., de la Torre, R., & Verdejo-Garcia, A. (2018). Cognitive and neuromodulation strategies for unhealthy eating and obesity: Systematic review and discussion of neurocognitive mechanisms. Neuroscience and Biobehavioral Reviews, 87, 161–191. doi:10.1016/j.neubiorev.2018.02.003CrossRefGoogle ScholarPubMed
Frank, G., Shott, M. E., Stoddard, J., Swindle, S., & Pryor, T. L. (2021). Association of brain reward response with body mass index and ventral striatal-hypothalamic circuitry among young women with eating disorders. JAMA Psychiatry, 78(10), 1123–1133. doi:10.1001/jamapsychiatry.2021.1580CrossRefGoogle ScholarPubMed
Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35(3), 346–355. doi:10.1002/mrm.1910350312CrossRefGoogle ScholarPubMed
Gao, M., Wong, C., Huang, H., Shao, R., Huang, R., Chan, C., & Lee, T. (2020). Connectome-based models can predict processing speed in older adults. NeuroImage, 223, 117290. doi:10.1016/j.neuroimage.2020.117290CrossRefGoogle ScholarPubMed
Gaudio, S., & Quattrocchi, C. C. (2012). Neural basis of a multidimensional model of body image distortion in anorexia nervosa. Neuroscience and Biobehavioral Reviews, 36(8), 1839–1847. doi:10.1016/j.neubiorev.2012.05.003CrossRefGoogle ScholarPubMed
Gaudio, S., Wiemerslage, L., Brooks, S. J., & Schiöth, H. B. (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. Neuroscience and Biobehavioral Reviews, 71, 578–589. doi:10.1016/j.neubiorev.2016.09.032CrossRefGoogle ScholarPubMed
Glashouwer, K. A., van der Veer, R., Adipatria, F., de Jong, P. J., & Vocks, S. (2019). The role of body image disturbance in the onset, maintenance, and relapse of anorexia nervosa: A systematic review. Clinical Psychology Review, 74, 101771. doi:10.1016/j.cpr.2019.101771CrossRefGoogle ScholarPubMed
Goldfarb, E. V., Rosenberg, M. D., Seo, D., Constable, R. T., & Sinha, R. (2020). Hippocampal seed connectome-based modeling predicts the feeling of stress. Nature Communications, 11(1), 2650. doi:10.1038/s41467-020-16492-2CrossRefGoogle ScholarPubMed
Greene, A. S., Gao, S., Scheinost, D., & Constable, R. T. (2018). Task-induced brain state manipulation improves prediction of individual traits. Nature Communications, 9(1), 2807. doi:10.1038/s41467-018-04920-3CrossRefGoogle ScholarPubMed
Hakamata, Y., Mizukami, S., Izawa, S., Moriguchi, Y., Hori, H., Kim, Y., … Tagaya, H. (2020). Basolateral amygdala connectivity with subgenual anterior cingulate cortex represents enhanced fear-related memory encoding in anxious humans. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(3), 301–310. doi:10.1016/j.bpsc.2019.11.008Google ScholarPubMed
Haynos, A. F., Camchong, J., Pearson, C. M., Lavender, J. M., Mueller, B. A., Peterson, C. B., … Lim, K. O. (2021). Resting-state hypoconnectivity of reward networks in binge eating disorder. Cerebral Cortex, 31(5), 2494–2504. doi:10.1093/cercor/bhaa369CrossRefGoogle ScholarPubMed
He, J., Cai, Z., & Fan, X. (2017). Prevalence of binge and loss of control eating among children and adolescents with overweight and obesity: An exploratory meta-analysis. International Journal of Eating Disorders, 50(2), 91–103. doi:10.1002/eat.22661CrossRefGoogle ScholarPubMed
He, L., Wei, D., Yang, F., Zhang, J., Cheng, W., Feng, J., … Qiu, J. (2021). Functional connectome prediction of anxiety related to the COVID-19 pandemic. The American Journal of Psychiatry, 178(6), 530–540. doi:10.1176/appi.ajp.2020.20070979CrossRefGoogle ScholarPubMed
Horien, C., Noble, S., Finn, E. S., Shen, X., Scheinost, D., Constable, R. T. (2018). Considering factors affecting the connectome-based identification process: Comment on Waller et al. NeuroImage, 169, 172–175. doi:10.1016/j.neuroimage.2017.12.045CrossRefGoogle ScholarPubMed
Horien, C., Shen, X., Scheinost, D., & Constable, R. T. (2019). The individual functional connectome is unique and stable over months to years. NeuroImage, 189, 676–687. doi:10.1016/j.neuroimage.2019.02.002CrossRefGoogle ScholarPubMed
Hsu, W. T., Rosenberg, M. D., Scheinost, D., Constable, R. T., & Chun, M. M. (2018). Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals. Social Cognitive and Affective Neuroscience, 13(2), 224–232. doi:10.1093/scan/nsy002CrossRefGoogle ScholarPubMed
Ibrahim, K., Noble, S., He, G., Lacadie, C., Crowley, M. J., McCarthy, G., … Sukhodolsky, D. G. (2021). Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling. Molecular Psychiatry , 27(2), 985–999. doi:10.1038/s41380-021-01317-5Google ScholarPubMed
Jackson, T., & Chen, H. (2007). Identifying the eating disorder symptomatic in China: The role of sociocultural factors and culturally defined appearance concerns. Journal of Psychosomatic Research, 62(2), 241–249. doi:10.1016/j.jpsychores.2006.09.010CrossRefGoogle Scholar
Jackson, T., & Chen, H. (2008). Predicting changes in eating disorder symptoms among adolescents in China: An 18-month prospective study. Journal of Clinical Child and Adolescent Psychology, 37(4), 874–885. doi:10.1080/15374410802359841CrossRefGoogle Scholar
Jackson, T., & Chen, H. (2010). Sociocultural experiences of bulimic and non-bulimic adolescents in a school-based Chinese sample. Journal of Abnormal Child Psychology, 38(1), 69–76. doi:10.1007/s10802-009-9350-0CrossRefGoogle Scholar
Jiang, R., Calhoun, V. D., Fan, L., Zuo, N., Jung, R., Qi, S., … Sui, J. (2020). Gender differences in connectome-based predictions of individualized intelligence quotient and sub-domain scores. Cerebral Cortex, 30(3), 888–900. doi:10.1093/cercor/bhz134CrossRefGoogle ScholarPubMed
Ju, Y., Horien, C., Chen, W., Guo, W., Lu, X., Sun, J., … Li, L. (2020). Connectome-based models can predict early symptom improvement in major depressive disorder. Journal of Affective Disorders, 273, 442–452. doi:10.1016/j.jad.2020.04.028CrossRefGoogle ScholarPubMed
Kotler, L. A., Cohen, P., Davies, M., Pine, D. S., & Walsh, B. T. (2001). Longitudinal relationships between childhood, adolescent, and adult eating disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 40(12), 1434–1440. doi:10.1097/00004583-200112000-00014CrossRefGoogle ScholarPubMed
Li, J., Biswal, B. B., Meng, Y., Yang, S., Duan, X., Cui, Q., … Liao, W. (2020). A neuromarker of individual general fluid intelligence from the white-matter functional connectome. Translational Psychiatry, 10(1), 147. doi:10.1038/s41398-020-0829-3CrossRefGoogle ScholarPubMed
Lichenstein, S. D., Scheinost, D., Potenza, M. N., Carroll, K. M., & Yip, S. W. (2021). Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling. Molecular Psychiatry, 26(8), 4383–4393. doi:10.1038/s41380-019-0586-yCrossRefGoogle ScholarPubMed
Liu, P., Yang, W., Zhuang, K., Wei, D., Yu, R., Huang, X., & Qiu, J. (2021). The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic. Neurobiology of Stress, 14, 100285. doi:10.1016/j.ynstr.2020.100285CrossRefGoogle ScholarPubMed
Lu, X., Li, T., Xia, Z., Zhu, R., Wang, L., Luo, Y. J., … Krueger, F. (2019). Connectome-based model predicts individual differences in propensity to trust. Human Brain Mapping, 40(6), 1942–1954. doi:10.1002/hbm.24503CrossRefGoogle ScholarPubMed
Luo, Y.-J., Jackson, T., Niu, G.-F., & Chen, H. (2020). Effects of gender and appearance comparisons on associations between media-based appearance pressure and disordered eating: Testing a moderated mediation model. Sex Roles, 82, 293–305. doi:10.1007/s11199-019-01058-4CrossRefGoogle Scholar
Mitchison, D., Mond, J., Bussey, K., Griffiths, S., Trompeter, N., Lonergan, A., … Hay, P. (2020). DSM-5 full syndrome, other specified, and unspecified eating disorders in Australian adolescents: Prevalence and clinical significance. Psychological Medicine, 50(6), 981–990. doi:10.1017/S0033291719000898CrossRefGoogle ScholarPubMed
Monteleone, A. M., Castellini, G., Volpe, U., Ricca, V., Lelli, L., Monteleone, P., & Maj, M. (2018). Neuroendocrinology and brain imaging of reward in eating disorders: A possible key to the treatment of anorexia nervosa and bulimia nervosa. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 80, 132–142. doi:10.1016/j.pnpbp.2017.02.020CrossRefGoogle Scholar
Muschelli, J., Nebel, M. B., Caffo, B. S., Barber, A. D., Pekar, J. J., & Mostofsky, S. H. (2014). Reduction of motion-related artifacts in resting state fMRI using aCompCor. NeuroImage, 96, 22–35. doi:10.1016/j.neuroimage.2014.03.028CrossRefGoogle ScholarPubMed
Oliva, R., Morys, F., Horstmann, A., Castiello, U., & Begliomini, C. (2020). Characterizing impulsivity and resting-state functional connectivity in normal-weight binge eaters. International Journal of Eating Disorders, 53(3), 478–488. doi:10.1002/eat.23212CrossRefGoogle ScholarPubMed
Phillipou, A., Abel, L. A., Castle, D. J., Hughes, M. E., Nibbs, R. G., Gurvich, C., & Rossell, S. L. (2016). Resting-state functional connectivity in anorexia nervosa. Psychiatry Research: Neuroimaging, 251, 45–52. doi:10.1016/j.pscychresns.2016.04.008CrossRefGoogle ScholarPubMed
Poldrack, R. A., Huckins, G., & Varoquaux, G. (2020). Establishment of best practices for evidence for prediction: A review. JAMA Psychiatry, 77(5), 534–540. doi:10.1001/jamapsychiatry.2019.3671CrossRefGoogle ScholarPubMed
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142–2154. doi:10.1016/j.neuroimage.2011.10.018CrossRefGoogle ScholarPubMed
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341. doi:10.1016/j.neuroimage.2013.08.048CrossRefGoogle ScholarPubMed
Razi, A., Kahan, J., Rees, G., & Friston, K. J. (2015). Construct validation of a DCM for resting state fMRI. NeuroImage, 106, 1–14. doi:10.1016/j.neuroimage.2014.11.027CrossRefGoogle ScholarPubMed
Ren, Z., Daker, R. J., Shi, L., Sun, J., Beaty, R. E., Wu, X., … Qiu, J. (2021). Connectome-based predictive modeling of creativity anxiety. NeuroImage, 225, 117469. doi:10.1016/j.neuroimage.2020.117469CrossRefGoogle ScholarPubMed
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19(1), 165–171. doi:10.1038/nn.4179CrossRefGoogle ScholarPubMed
Rosenberg, M. D., Hsu, W. T., Scheinost, D., Todd Constable, R., & Chun, M. M. (2018). Connectome-based models predict separable components of attention in novel individuals. Journal of Cognitive Neuroscience, 30(2), 160–173. doi:10.1162/jocn_a_01197CrossRefGoogle ScholarPubMed
Rosenberg, M. D., Scheinost, D., Greene, A. S., Avery, E. W., Kwon, Y. H., Finn, E. S., … Chun, M. M. (2020). Functional connectivity predicts changes in attention observed across minutes, days, and months. Proceedings of the National Academy of Sciences of the United States of America, 117(7), 3797–3807. doi:10.1073/pnas.1912226117CrossRefGoogle ScholarPubMed
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., … Wolf, D. H. (2013). An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. NeuroImage, 64, 240–256. doi:10.1016/j.neuroimage.2012.08.052CrossRefGoogle ScholarPubMed
Scheinost, D., Noble, S., Horien, C., Greene, A. S., Lake, E. M., Salehi, M., … Constable, R. T. (2019). Ten simple rules for predictive modeling of individual differences in neuroimaging. NeuroImage, 193, 35–45. doi:10.1016/j.neuroimage.2019.02.057CrossRefGoogle ScholarPubMed
Shapiro, A., Johnson, S. L., Sutton, B., Legget, K. T., Dabelea, D., & Tregellas, J. R. (2019). Eating in the absence of hunger in young children is related to brain reward network hyperactivity and reduced functional connectivity in executive control networks. Pediatric Obesity, 14(6), e12502. doi:10.1111/ijpo.12502CrossRefGoogle ScholarPubMed
Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., & Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3), 506–518. doi:10.1038/nprot.2016.178CrossRefGoogle ScholarPubMed
Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403–415. doi:10.1016/j.neuroimage.2013.05.081CrossRefGoogle Scholar
Siddiqi, S. H., Taylor, S. F., Cooke, D., Pascual-Leone, A., George, M. S., & Fox, M. D. (2020). Distinct symptom-specific treatment targets for circuit-based neuromodulation. The American Journal of Psychiatry, 177(5), 435–446. doi:10.1176/appi.ajp.2019.19090915CrossRefGoogle ScholarPubMed
Simon, J. J., Skunde, M., Wu, M., Schnell, K., Herpertz, S. C., Bendszus, M., … Friederich, H. C. (2015). Neural dissociation of food- and money-related reward processing using an abstract incentive delay task. Social Cognitive and Affective Neuroscience, 10(8), 1113–1120. doi:10.1093/scan/nsu162CrossRefGoogle ScholarPubMed
Smith, K. E., Luo, S., & Mason, T. B. (2021). A systematic review of neural correlates of dysregulated eating associated with obesity risk in youth. Neuroscience and Biobehavioral Reviews, 124, 245–266. doi:10.1016/j.neubiorev.2021.02.013CrossRefGoogle ScholarPubMed
Sparti, C., Santomauro, D., Cruwys, T., Burgess, P., & Harris, M. (2019). Disordered eating among Australian adolescents: Prevalence, functioning, and help received. International Journal of Eating Disorders, 52(3), 246–254. doi:10.1002/eat.23032CrossRefGoogle ScholarPubMed
Sprooten, E., Rasgon, A., Goodman, M., Carlin, A., Leibu, E., Lee, W. H., & Frangou, S. (2017). Addressing reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in common mental disorders. Human Brain Mapping, 38(4), 1846–1864. doi:10.1002/hbm.23486CrossRefGoogle ScholarPubMed
Stice, E., Fisher, M., & Martinez, E. (2004). Eating disorder diagnostic scale: Additional evidence of reliability and validity. Psychological Assessment, 16(1), 60–71. doi:10.1037/1040-3590.16.1.60CrossRefGoogle ScholarPubMed
Stice, E., Gau, J. M., Rohde, P., & Shaw, H. (2017). Risk factors that predict future onset of each DSM-5 eating disorder: Predictive specificity in high-risk adolescent females. Journal of Abnormal Psychology, 126(1), 38–51. doi:10.1037/abn0000219CrossRefGoogle ScholarPubMed
Stice, E., Telch, C. F., & Rizvi, S. L. (2000). Development and validation of the eating disorder diagnostic scale: A brief self-report measure of anorexia, bulimia, and binge-eating disorder. Psychological Assessment, 12(2), 123–131. doi:10.1037//1040-3590.12.2.123CrossRefGoogle Scholar
Takagi, Y., Sakai, Y., Abe, Y., Nishida, S., Harrison, B. J., Martínez-Zalacaín, I., … Tanaka, S. C. (2018). A common brain network among state, trait, and pathological anxiety from whole-brain functional connectivity. NeuroImage, 172, 506–516. doi:10.1016/j.neuroimage.2018.01.080CrossRefGoogle ScholarPubMed
Tanofsky-Kraff, M., Schvey, N. A., & Grilo, C. M. (2020). A developmental framework of binge-eating disorder based on pediatric loss of control eating. The American Psychologist, 75(2), 189–203. doi:10.1037/amp0000592CrossRefGoogle ScholarPubMed
Treasure, J., Duarte, T. A., & Schmidt, U. (2020). Eating disorders. The Lancet, 395(10227), 899–911. doi:10.1016/S0140-6736(20)30059-3CrossRefGoogle ScholarPubMed
Wang, D., Li, M., Wang, M., Schoeppe, F., Ren, J., Chen, H., … Liu, H. (2020). Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness. Molecular Psychiatry, 25(9), 2119–2129. doi:10.1038/s41380-018-0276-1CrossRefGoogle ScholarPubMed
Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience, 9, 386. doi:10.3389/fnhum.2015.00386Google ScholarPubMed
Wang, Z., Goerlich, K. S., Ai, H., Aleman, A., Luo, Y. J., & Xu, P. (2021). Connectome-based predictive modeling of individual anxiety. Cerebral Cortex, 31(6), 3006–3020. doi:10.1093/cercor/bhaa407CrossRefGoogle ScholarPubMed
Wu, X., Yang, Q., Xu, C., Huo, H., Seger, C. A., Peng, Z., & Chen, Q. (2022). Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder. Cerebral Cortex, Advance online publication. doi:10.1093/cercor/bhac145Google Scholar
Yan, C. G., Wang, X. D., Zuo, X. N., & Zang, Y. F. (2016). DPABI: Data processing & analysis for (Resting-state) brain imaging. Neuroinformatics, 14(3), 339–351. doi:10.1007/s12021-016-9299-4CrossRefGoogle ScholarPubMed
Yang, W., Zhuang, K., Liu, P., Guo, Y., Chen, Q., Wei, D., & Qiu, J. (2021). Memory suppression ability can be robustly predicted by the internetwork communication of frontoparietal control network. Cerebral Cortex, 31(7), 3451–3461. doi:10.1093/cercor/bhab024CrossRefGoogle ScholarPubMed
Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. doi:10.1177/1745691617693393CrossRefGoogle ScholarPubMed
Yip, S. W., Kiluk, B., & Scheinost, D. (2020). Toward addiction prediction: An overview of cross-validated predictive modeling findings and considerations for future neuroimaging research. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(8), 748–758. doi:10.1016/j.bpsc.2019.11.001Google ScholarPubMed
Yip, S. W., Scheinost, D., Potenza, M. N., & Carroll, K. M. (2019). Connectome-based prediction of cocaine abstinence. The American Journal of Psychiatry, 176(2), 156–164. doi:10.1176/appi.ajp.2018.17101147CrossRefGoogle ScholarPubMed
Yoo, K., Rosenberg, M. D., Hsu, W. T., Zhang, S., Li, C. R., Scheinost, D., … Chun, M. M. (2018). Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. NeuroImage, 167, 11–22. doi:10.1016/j.neuroimage.2017.11.010CrossRefGoogle ScholarPubMed
Zhang, C., Dougherty, C. C., Baum, S. A., White, T., & Michael, A. M. (2018). Functional connectivity predicts gender: Evidence for gender differences in resting brain connectivity. Human Brain Mapping, 39(4), 1765–1776. doi:10.1002/hbm.23950CrossRefGoogle ScholarPubMed
Zhang, D., Zhou, L., Yang, A., Li, S., Chang, C., Liu, J., & Zhou, K. (2022). A connectome-based neuromarker of nonverbal number acuity and arithmetic skills. Cerebral Cortex, Advance online publication. doi:10.1093/cercor/bhac108Google Scholar
Zhang, Z., Robinson, L., Jia, T., Quinlan, E. B., Tay, N., Chu, C., … Desrivières, S. (2021). Development of disordered eating behaviors and comorbid depressive symptoms in adolescence: Neural and psychopathological predictors. Biological Psychiatry, 90(12), 853–862. doi:10.1016/j.biopsych.2020.06.003CrossRefGoogle ScholarPubMed
Supplementary material: File

Chen et al. supplementary material

Chen et al. supplementary material
Download Chen et al. supplementary material(File)
File 18.3 MB