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Cited by 18Functional Brain Connectivity StudiesRegulation of Appetite and ObesityEating Disorders and Behaviors

Neural correlates of future weight loss reveal a possible role for brain-gastric interactions

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Levakov, Gidon, Kaplan, Alon, Yaskolka Meir, Anat, Rinott, Ehud, Tsaban, Gal, Zelicha, Hila, Meiran, Nachshon, Shelef, Ilan +2 more·2021·NeuroImage
DOI 10.1016/j.neuroimage.2020.117403
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Abstract

Lifestyle dietary interventions are an essential practice in treating obesity, hence neural factors that may assist in predicting individual treatment success are of great significance. Here, in a prospective, open-label, three arms study, we examined the correlation between brain resting-state functional connectivity measured at baseline and weight loss following 6 months of lifestyle intervention in 92 overweight participants. We report a robust subnetwork composed mainly of sensory and motor cortical regions, whose edges correlated with future weight loss. This effect was found regardless of intervention group. Importantly, this main finding was further corroborated using a stringent connectivity-based prediction model assessed with cross-validation thus attesting to its robustness. The engagement of senso-motor regions in this subnetwork is consistent with the over-sensitivity to food cues theory of weight regulation. Finally, we tested an additional hypothesis regarding the role of brain-gastric interaction in this subnetwork, considering recent findings of a cortical network synchronized with gastric activity. Accordingly, we found a significant spatial overlap with the subnetwork reported in the present study. Moreover, power in the gastric basal electric frequency within our reported subnetwork negatively correlated with future weight loss. This finding was specific to the weight loss related subnetwork and to the gastric basal frequency. These findings should be further corroborated by combining direct recordings of gastric activity in future studies. Taken together, these intriguing results may have important implications for our understanding of the etiology of obesity and the mechanism of response to dietary intervention.

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  • In humans, the network predicting weight loss overlaps with the gastric network.

    “In humans, the network predicting weight loss overlaps with the gastric network 184 ,”

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  1. 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.2007.04.042 . · DOI · Paper page
  2. Boly, M., Balteau, E., Schnakers, C., Degueldre, C., Moonen, G., Luxen, A., …, Laureys, S., 2007. Baseline brain activity fluctuations predict somatosensory perception in hu- mans. Proc. Natl. Acad. Sci. 104 (29), 12187-12192. doi: 10.1073/pnas.0611404104 . · DOI · Paper page
  3. Bullmore, E., Sporns, O., 2009. Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience. Nature Publishing Group doi: 10.1038/nrn2575 . · DOI · Paper page
  4. Burger, K.S., Stice, E., 2013. Elevated energy intake is correlated with hyperresponsivity in attentional, gustatory, and reward brain regions while anticipating palatable food receipt. Am. J. Clin. Nutr. 97 (6), 1188-1194. doi: 10.3945/ajcn.112.055285 . · DOI
  5. Cameron, C., Sharad, S., Brian, C., Ranjeet, K., Satrajit, G., Chaogan, Y., …, Michael, M., 2013. Towards automated analysis of connectomes: the config- urable pipeline for the analysis of connectomes (C-PAC). Neuroinformatics 7. doi: 10.3389/conf.fninf.2013.09.00042 . · DOI
  6. Cao, J., Lu, K.-H., Oleson, S.T., Phillips, R.J., Jaffey, D., Hendren, C.L., …, Liu, Z., 2019. Gastric stimulation drives fast BOLD responses of neural origin. NeuroImage 197, 200-211. doi: 10.1016/J.NEUROIMAGE.2019.04.064 . · DOI · Paper page
  7. Contreras-Rodríguez, O., Martín-Pérez, C., Vilar-López, R., Verdejo-Garcia, A., 2017. Ven- tral and dorsal striatum networks in obesity: link to food craving and weight gain. Biol. Psychiatry 81 (9), 789-796. doi: 10.1016/J.BIOPSYCH.2015.11.020 . · DOI
  8. Curtis, B.J., Williams, P.G., Jones, C.R., Anderson, J.S., 2016. Sleep duration and resting fMRI functional connectivity: examination of short sleepers with and without per- ceived daytime dysfunction. Brain Behav. 6 (12), e00576. doi: 10.1002/brb3.576 . · DOI
  9. D'Archivio, M., Filesi, C., Varì, R., Scazzocchio, B., Masella, R., 2010. Bioavailability of the polyphenols: status and controversies. Int. J. Mol. Sci. doi: 10.3390/ijms11041321 . · DOI · Paper page
  10. Daducci, A., Gerhard, S., Griffa, A., Lemkaddem, A., Cammoun, L., Gigandet, X., …, Thiran, J.P., 2012. The connectome mapper: an open-source processing pipeline to map connectomes with MRI. PLoS One 7 (12), e48121. doi: 10.1371/jour- nal.pone.0048121 . · DOI
  11. de Araujo, I.E., Oliveira-Maia, A.J., Sotnikova, T.D., Gainetdinov, R.R., Caron, M.G., Nicolelis, M.A.L., Simon, S.A., 2008. Food reward in the absence of taste receptor signaling. Neuron 57 (6), 930-941. doi: 10.1016/j.neuron.2008.01.032 . · DOI · Paper page
  12. DiFeliceantonio, A.G., Coppin, G., Rigoux, L., Edwin Thanarajah, S., Dagher, A., Tittge- meyer, M., Small, D.M., 2018. Supra-additive effects of combining fat and carbohy- drate on food reward. Cell Metab. 28 (1), 33-44. doi: 10.1016/J.CMET.2018.05.018 . · DOI
  13. Dong, D., Jackson, T., Wang, Y., Chen, H., 2015. Spontaneous regional brain activity links restrained eating to later weight gain among young women. Biol. Psychol. 109, 176- 183. doi: 10.1016/j.biopsycho.2015.05.003 . · DOI
  14. Finn, E.S., Shen, X., Scheinost, D., Rosenberg, M.D., Huang, J., Chun, M.M., …, Constable, R.T., 2015. Functional connectome fingerprinting: identifying individ- uals using patterns of brain connectivity. Nat. Neurosci. 18 (11), 1664-1671. doi: 10.1038/nn.4135 . · DOI · Paper page
  15. Fischl, B., Sereno, M.I., Dale, A.M., 1999. Cortical surface-based analysis: II. Inflation, flattening, and a surface-based coordinate system. NeuroImage. Academic Press doi: 10.1006/nimg.1998.0396 . · DOI · Paper page
  16. Fischl, B., Van Der Kouwe, A., Destrieux, C., Halgren, E., Ségonne, F., Salat, D.H., …, Dale, A.M., 2004. Automatically parcellating the human cerebral cortex. Cereb. Cortex 14 (1), 11-22. doi: 10.1093/cercor/bhg087 . · DOI
  17. Friedman, N.P., Miyake, A., 2017. Unity and diversity of executive functions: in- dividual differences as a window on cognitive structure. Cortex 86, 186-204. doi: 10.1016/J.CORTEX.2016.04.023 . · DOI · Paper page
  18. García-García, I., Jurado, M.Á., Garolera, M., Segura, B., Sala-Llonch, R., Marqués- Iturria, I., …, Junqué, C., 2013. Alterations of the salience network in obe- sity: a resting-state fMRI study. Hum. Brain Mapp. 34 (11), 2786-2797. doi: 10.1002/hbm.22104 . · DOI · Paper page
  19. Geha, P.Y., Aschenbrenner, K., Felsted, J., O'Malley, S.S., Small, D.M., 2013. Altered hypothalamic response to food in smokers. Am. J. Clin. Nutr. 97 (1), 15-22. doi: 10.3945/ajcn.112.043307 . · DOI
  20. Gettens, K.M., Gorin, A.A., 2017. Executive function in weight loss and weight loss main- tenance: a conceptual review and novel neuropsychological model of weight control. J. Behav. Med. doi: 10.1007/s10865-017-9831-5 , Springer US, https://doi.org/. · DOI
  21. Ghil, M., Allen, M.R., Dettinger, M.D., Ide, K., Kondrashov, D., Mann, M.E., …, Yiou, P., 2002. Advanced spectral methods for climatic time series. Rev. Geophys. 40 (1), 1003. doi: 10.1029/2000RG000092 . · DOI
  22. Gordon, S. , Todder, D. , Deutsch, I. , Garbi, D. , Neuropsychologia, N.G.-, 2018. In: Are Resting State Spectral Power Measures Related to Executive Functions in Healthy Young Adults? ‫‬ , 108. Elsevier, pp. 61-72 Retrieved from .
  23. Gorgolewski, K., Burns, C.D., Madison, C., Clark, D., Halchenko, Y.O., Waskom, M.L., Ghosh, S.S., 2011. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform. 5, 13. doi: 10.3389/fn- inf.2011.00013 . · DOI
  24. Grundy, D., 2002. Neuroanatomy of visceral nociception: vagal and splanchnic afferent. Gut 51 (Supplement 1), i2-i5. doi: 10.1136/gut.51.suppl_1.i2 . · DOI
  25. Grundy, David, Brookes, S, 2011. Neural control of gastrointestinal func- tion. Colloq. Ser. Integr. Syst. Physiol.: Mol. Funct. 3 (9), 1-134. doi: 10.4199/C00048ED1V01Y201111ISP030 . · DOI
  26. Guillery, R.W., Sherman, S.M., 2002. Thalamic Relay functions and their role in corticocor- tical communication: generalizations from the visual system. Neuron 33 (2), 163-175. doi: 10.1016/s0896-6273(01)00582-7 . · DOI
  27. Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Van Wedeen, J., Sporns, O., 2008. Mapping the structural core of human cerebral cortex. PLoS Biol. 6 (7), 1479-1493. doi: 10.1371/journal.pbio.0060159 . · DOI · Paper page
  28. Han, J.E., Boachie, N., Garcia-Garcia, I., Michaud, A., Dagher, A., 2018. Neural correlates of dietary self-control in healthy adults: a meta-analysis of functional brain imaging studies. Physiol. Behav. 192, 98-108. doi: 10.1016/J.PHYSBEH.2018.02.037 . · DOI
  29. Janssen, P., Vanden Berghe, P., Verschueren, S., Lehmann, A., Depoortere, I., Tack, J., 2011. Review article: the role of gastric motility in the control of food intake. Ali- mentary Pharmacology and Therapeutics. WileyBlackwell (10.1111), https://doi.org/ doi: 10.1111/j.1365-2036.2011.04609.x . · DOI
  30. Kroemer, N.B., Sun, X., Veldhuizen, M.G., Babbs, A.E., de Araujo, I.E., Small, D.M., 2016. Weighing the evidence: variance in brain responses to milkshake receipt is predictive of eating behavior. NeuroImage 128, 273-283. doi: 10.1016/J.NEUROIMAGE.2015.12.031 . · DOI
  31. Lavagnino, L., Arnone, D., Cao, B., Soares, J.C., Selvaraj, S., 2016. Inhibitory control in obesity and binge eating disorder: a systematic review and meta-analysis of neurocognitive and neuroimaging studies. Neurosci. Biobehav. Rev. 68, 714-726. doi: 10.1016/J.NEUBIOREV.2016.06.041 . · DOI
  32. Lerman-Sinkoff, D.B., Sui, J., Rachakonda, S., Kandala, S., Calhoun, V.D., Barch, D.M., 2017. Multimodal neural correlates of cognitive control in the Human Connectome Project. NeuroImage 163, 41-54. doi: 10.1016/J.NEUROIMAGE.2017.08.081 . · DOI
  33. Little, T.J., McKie, S., Jones, R.B., D'Amato, M., Smith, C., Kiss, O., …, McLaughlin, J.T., 2014. Mapping glucose-mediated gut-to-brain signalling pathways in humans. Neu- roImage 96, 1-11. doi: 10.1016/J.NEUROIMAGE.2014.03.059 . · DOI
  34. MacLean, P.S., Rothman, A.J., Nicastro, H.L., Czajkowski, S.M., Agurs-Collins, T., Rice, E.L., …, Loria, C.M., 2018. The accumulating data to optimally predict obe- sity treatment (ADOPT) core measures project: rationale and approach. Obesity 26, S6-S15. doi: 10.1002/oby.22154 . · DOI
  35. Mokdad, A.H., Serdula, M.K., Dietz, W.H., Bowman, B.A., Marks, J.S., Koplan, J.P., 1999. The spread of the obesity epidemic in the United States, 1991-1998. JAMA 282 (16), 1519. doi: 10.1001/jama.282.16.1519 . · DOI
  36. Mokhtari, F., Rejeski, W.J., Zhu, Y., Wu, G., Simpson, S.L., Burdette, J.H., Laurienti, P.J., 2018. Dynamic fMRI networks predict success in a behav- ioral weight loss program among older adults. NeuroImage 173, 421-433. doi: 10.1016/J.NEUROIMAGE.2018.02.025 . · DOI
  37. Neseliler, S., Hu, W., Larcher, K., Zacchia, M., Dadar, M., Scala, S.G., …, Dagher, A., 2019. Neurocognitive and hormonal correlates of voluntary weight loss in humans. Cell Metabol. 29 (1), 39-49. doi: 10.1016/J.CMET.2018.09.024 . · DOI
  38. Nomi, J.S., Vij, S.G., Dajani, D.R., Steimke, R., Damaraju, E., Rachakonda, S., …, Ud- din, L.Q., 2017. Chronnectomic patterns and neural flexibility underlie executive func- tion. NeuroImage 147, 861-871. doi: 10.1016/j.neuroimage.2016.10.026 . · DOI · Paper page
  39. Ozaki, N., Sengupta, J.N., Gebhart, G.F., 1999. Mechanosensitive properties of gas- tric vagal afferent fibers in the rat. J. Neurophysiol. 82 (5), 2210-2220. doi: 10.1152/jn.1999.82.5.2210 . · DOI
  40. Pang, J., Zhang, Z., Zheng, T.Z., Bassig, B.A., Mao, C., Liu, X., …, Peng, Y., 2016. Green tea consumption and risk of cardiovascular and ischemic related diseases: a meta-analysis. Int. J. Cardiol. 202, 967-974. doi: 10.1016/j.ijcard.2014.12.176 . · DOI
  41. Park, M.I., Camilleri, M., 2005. Gastric motor and sensory functions in obesity. Obesity Research. Wiley-Blackwell doi: 10.1038/oby.2005. · DOI
  42. Pigarev, I. , Almirall, H. , Pigareva, M.L. , Bautista, V. , Barcia, C. , Herrero, M.T. , 2006. Vis- ceral signals reach visual cortex during slow wave sleep: study in monkeys. Acta Neu- robiol. Exp. 66 (1), 69-73 . · DOI
  43. Pigarev, I.N., 1994. Neurons of visual cortex respond to visceral stimulation during slow wave sleep. Neuroscience 62 (4), 1237-1243. doi: 10.1016/0306-4522(94)90355-7 . · DOI · Paper page
  44. 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.2013.08.048 . · DOI · Paper page
  45. Richter, C.G., Babo-Rebelo, M., Schwartz, D., Tallon-Baudry, C., 2017. Phase-amplitude coupling at the organism level: the amplitude of spontaneous alpha rhythm fluctua- tions varies with the phase of the infra-slow gastric basal rhythm. NeuroImage 146, 951-958. doi: 10.1016/j.neuroimage.2016.08.043 . · DOI · Paper page
  46. Rokem, A. , Trumpis, M. , Perez, F. , 2009. Nitime: time-series analysis for neuroimaging data. In: Proceedings of the 8th Python in Science Conference, pp. 68-75 .
  47. Rubinov, M., Sporns, O., 2010. Complex network measures of brain con- nectivity: uses and interpretations. NeuroImage 52 (3), 1059-1069. doi: 10.1016/j.neuroimage.2009.10.003 . · DOI · Paper page
  48. Salthouse, T.A., 2005. Relations between cognitive abilities and measures of executive functioning. Neuropsychology 19 (4), 532-545. doi: 10.1037/0894-4105.19.4.532 . · DOI
  49. Sanders, K.M., Koh, S.D., Ward, S.M., 2006. Interstitial cells of Cajal as pacemakers in the gastrointestinal tract. Annu. Rev. Physiol. 68 (1), 307-343. doi: 10.1146/an- nurev.physiol.68.040504.094718 . · DOI · Paper page
  50. Sarna, S.K., 1985. Cyclic motor activity; migrating motor complex: 1985. Gastroenterology 89 (4), 894-913. doi: 10.1016/0016-5085(85)90589-X . · DOI
  51. Sewaybricker, L.E., Melhorn, S.J., Askren, M.K., Webb, M.F., Tyagi, V., De Leon, M.R.B., …, Schur, E.A., 2019. Salience network connectivity is reduced by a meal and influenced by genetic background and hypothalamic gliosis. Int. J. Obes. 1-11. doi: 10.1038/s41366-019-0361-9 . · DOI
  52. Shai, I., Schwarzfuchs, D., Henkin, Y., Shahar, D.R., Witkow, S., Greenberg, I., …, Stampfer, M.J., 2008. Weight loss with a low-carbohydrate, mediterranean, or low-fat diet. N. Engl. J. Med. 359 (3), 229-241. doi: 10.1056/NEJMoa0708681 . · DOI · Paper page
  53. 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. Nat. Protoc. 12 (3), 506-518. doi: 10.1038/nprot.2016.178 . · DOI
  54. Spoormaker, V.I., Schröter, M.S., Gleiser, P.M., Andrade, K.C., Dresler, M., Wehrle, R., …, Czisch, M., 2010. Development of a large-scale functional brain network dur- ing human non-rapid eye movement sleep. J. Neurosci. 30 (34), 11379-11387. doi: 10.1523/JNEUROSCI.2015-10.2010 . · DOI · Paper page
  55. Sporns, O., Tononi, G., Kötter, R., 2005. The human connectome: a structural descrip- tion of the human brain. PLoS Comput. Biol. 1 (4), 0245-0251. doi: 10.1371/jour- nal.pcbi.0010042 . · DOI · Paper page
  56. Steinert, R.E., Meyer-Gerspach, A.C., Beglinger, C., 2012. The role of the stomach in the control of appetite and the secretion of satiation peptides. AJP: Endocrinol. Metab. 302 (6), E666-E673. doi: 10.1152/ajpendo.00457.2011 . · DOI
  57. Stice, E., Burger, K., 2019. Neural vulnerability factors for obesity. Clin. Psychol. Rev. 68, 38-53. doi: 10.1016/J.CPR.2018.12.002 . · DOI
  58. Stice, E., Yokum, S., 2018. Relation of neural response to palatable food tastes and images to future weight gain: Using bootstrap sampling to examine replicability of neuroimag- ing findings. NeuroImage 183, 522-531. doi: 10.1016/j.neuroimage.2018.08.035 . · DOI
  59. Tack, J., Deloose, E., Ang, D., Scarpellini, E., Vanuytsel, T., Van Oudenhove, L., De- poortere, I., 2016. Motilin-induced gastric contractions signal hunger in man. Gut 65 (2), 214-224. doi: 10.1136/gutjnl-2014-308472 . · DOI
  60. Tagliazucchi, E., Laufs, H., 2014. Decoding wakefulness levels from typical fMRI resting- state data reveals reliable drifts between wakefulness and sleep. Neuron 82 (3), 695- 708. doi: 10.1016/J.NEURON.2014.03.020 . · DOI
  61. Thomson, D.J., 1982. Spectrum estimation and harmonic analysis. Proc. IEEE 70 (9), 1055-1096. doi: 10.1109/PROC.1982.12433 . · DOI
  62. Tomasi, D., Wang, G.J., Wang, R., Backus, W., Geliebter, A., Telang, F., …, Volkow, N.D., 2009. Association of body mass and brain activation during gastric distention: Impli- cations for obesity. PLoS One 4 (8), e6847. doi: 10.1371/journal.pone.0006847 . · DOI · Paper page
  63. Van Dijk, K.R.A., Sabuncu, M.R., Buckner, R.L., 2012. The influence of head mo- tion on intrinsic functional connectivity MRI. NeuroImage 59 (1), 431-438. doi: 10.1016/J.NEUROIMAGE.2011.07.044 . · DOI · Paper page
  64. Van Oudenhove, L., Vandenberghe, J., Dupont, P., Geeraerts, B., Vos, R., Bor- mans, G., …, Tack, J., 2009. Cortical deactivations during gastric fundus disten- sion in health: Visceral pain-specific response or attenuation of "default mode " brain function? A H215O-PET study. Neurogastroenterol. Motil. 21 (3), 259-271. doi: 10.1111/j.1365-2982.2008.01196.x . · DOI
  65. Verhaeghen, P., 2011. Aging and executive control: reports of a demise greatly exagger- ated. Curr. Direct. Psychol. Sci. 20 (3), 174-180. doi: 10.1177/0963721411408772 . · DOI
  66. Wang, G.J., Tomasi, D., Backus, W., Wang, R., Telang, F., Geliebter, A., …, Volkow, N.D., 2008. Gastric distention activates satiety circuitry in the human brain. NeuroImage 39 (4), 1824-1831. doi: 10.1016/j.neuroimage.2007.11.008 . · DOI · Paper page
  67. Wang, Y., Beydoun, M.A., Liang, L., Caballero, B., Kumanyika, S.K., 2008. Will all amer- icans become overweight or obese? Estimating the progression and cost of the US obesity epidemic. Obesity 16 (10), 2323-2330. doi: 10.1038/oby.2008.351 . · DOI
  68. Wasylyshyn, C., Verhaeghen, P., Sliwinski, M.J., 2011. Aging and task switching: a meta- analysis. Psychol. Aging 26 (1), 15-20. doi: 10.1037/a0020912 . · DOI
  69. Weygandt, M., Mai, K., Dommes, E., Leupelt, V., Hackmack, K., Kahnt, T., …, Haynes, J.D., 2013. The role of neural impulse control mechanisms for dietary success in obesity. NeuroImage 83, 669-678. doi: 10.1016/j.neuroimage.2013.07.028 . · DOI
  70. Yaskolka Meir, A., Tsaban, G., Zelicha, H., Rinott, E., Kaplan, A., Youngster, I., …, Shai, I., 2019. A green-Mediterranean diet, supplemented with Mankai Duckweed, preserves iron-homeostasis in humans and is efficient in reversal of anemia in rats. J. Nutr. 149 (6), 1004-1011. doi: 10.1093/jn/nxy321 . · DOI · Paper page
  71. Yeo, B.T.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., …, Roland, P., 2011. The organization of the human cerebral cortex esti- mated by intrinsic functional connectivity. J. Neurophysiol. 106 (3), 1125-1165. doi: 10.1152/jn.00338.2011 . · DOI · Paper page

Cited by

Sorted by citations

Authors
Levakov, G., Kaplan, A., Yaskolka Meir, A., Rinott, E., Tsaban, G., Zelicha, H., Meiran, N., Shelef, I., Shai, I., & Avidan, G.
Affiliations
Ben-Gurion University of the Negev (IL)
Corresponding author
Levakov, Gidon
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2021-01
Volume
224
Pages
117403
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2020.117403
OpenAlex
W3088094218
Cited by
18
References
7
Citations by year
2026: 2 · 2025: 5 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 4
FWCI
1.35
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • California Walnut Commission
  • Ministry of Science and Technology, Israel (3-13604)
  • Deutsche Forschungsgemeinschaft (209933838 \u2013 SFB 1052, SFB 1052)
  • Ministry of Health, State of Israel (87472511)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Life Sciences › Neuroscience › Endocrine and Autonomic Systems › Regulation of Appetite and Obesity
  • Social Sciences › Psychology › Clinical Psychology › Eating Disorders and Behaviors
Keywords
weight loss prediction, resting-state functional connectivity, sensorimotor cortex, lifestyle intervention, obesity
MeSH terms
Clinical Decision Rules, Adult, Brain, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Obesity, Stomach, Weight Loss, Exercise, Treatment Outcome, Diet, Mediterranean, Overweight, Polyphenols, Functional Neuroimaging, Connectome, Sensorimotor Cortex

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