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Neural mechanisms supporting maladaptive food choices in anorexia nervosa

Abstract

People routinely make poor choices, despite knowledge of negative consequences. The authors found that individuals with anorexia nervosa, who make maladaptive food choices to the point of starvation, engaged the dorsal striatum more than healthy controls when making choices about what to eat, and that activity in fronto-striatal circuits was correlated with their actual food consumption in a meal the next day.

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Figure 1: Food choice task and behavioral results.
Figure 2: Distinct neural systems engaged in food choice.
Figure 3: Food choice is related to functional connectivity between the striatum and the dlPFC.

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References

  1. Marteau, T.M., Hollands, G.J. & Fletcher, P.C. Science 337, 1492–1495 (2012).

    Article  CAS  Google Scholar 

  2. Walsh, B.T. Physiol. Behav. 104, 525–529 (2011).

    Article  CAS  Google Scholar 

  3. Hadigan, C.M. et al. Int. J. Eat. Disord. 28, 284–292 (2000).

    Article  CAS  Google Scholar 

  4. Fernstrom, M.H., Weltzin, T.E., Neuberger, S., Srinivasagam, N. & Kaye, W.H. Biol. Psychiatry 36, 696–702 (1994).

    Article  CAS  Google Scholar 

  5. Bruch, H. The Golden Cage: The Enigma of Anorexia Nervosa (Vintage Books, 1978).

  6. Schebendach, J.E. et al. Am. J. Clin. Nutr. 87, 810–816 (2008).

    Article  CAS  Google Scholar 

  7. Sysko, R., Walsh, B.T., Schebendach, J. & Wilson, G.T. Am. J. Clin. Nutr. 82, 296–301 (2005).

    Article  CAS  Google Scholar 

  8. Steinglass, J., Foerde, K., Kostro, K., Shohamy, D. & Walsh, B.T. Int. J. Eat. Disord. 48, 59–66 (2015).

    Article  Google Scholar 

  9. Hare, T.A., Camerer, C.F. & Rangel, A. Science 324, 646–648 (2009).

    Article  CAS  Google Scholar 

  10. Drewnowski, A., Halmi, K.A., Pierce, B., Gibbs, J. & Smith, G.P. Am. J. Clin. Nutr. 46, 442–450 (1987).

    Article  CAS  Google Scholar 

  11. Mayer, L.E., Schebendach, J., Bodell, L.P., Shingleton, R.M. & Walsh, B.T. Int. J. Eat. Disord. 45, 290–293 (2012).

    Article  Google Scholar 

  12. Stoner, S.A., Fedoroff, I.C., Andersen, A.E. & Rolls, B.J. Int. J. Eat. Disord. 19, 13–22 (1996).

    Article  CAS  Google Scholar 

  13. De Young, K.P. et al. Int. J. Eat. Disord. 46, 849–854 (2013).

    Article  Google Scholar 

  14. Walsh, B.T., Kissileff, H.R., Cassidy, S.M. & Dantzic, S. Arch. Gen. Psychiatry 46, 54–58 (1989).

    Article  CAS  Google Scholar 

  15. Oudijn, M.S., Storosum, J.G., Nelis, E. & Denys, D. BMC Psychiatry 13, 277 (2013).

    Article  Google Scholar 

  16. Decker, J.H., Figner, B. & Steinglass, J.E. Biol. Psychiatry published online, 10.1016/j.biopsych.2014.12.016 (23 December 2014).

  17. Uher, R. et al. Biol. Psychiatry 54, 934–942 (2003).

    Article  Google Scholar 

  18. Wagner, A. et al. Neuropsychopharmacology 33, 513–523 (2008).

    Article  Google Scholar 

  19. Graybiel, A.M. Annu. Rev. Neurosci. 31, 359–387 (2008).

    Article  CAS  Google Scholar 

  20. Ellison, Z. et al. Lancet 352, 1192 (1998).

    Article  CAS  Google Scholar 

  21. American Psychiatric Association: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (American Psychiatric Association, 2013).

  22. Spitzer, R.L., Williams, J.B.W. & Gibbon, M. Structured Clinical Interview for DSM-IV-R (SCID) (New York State Psychiatric Institute, Biometrics Research, 1987).

  23. Fairburn, C.G. & Cooper, P.J. in Binge Eating: Nature, Assessment, and Treatment (eds. C.G. Fairburn & G.T. Wilson) 317–360 (Guilford Press, 1993).

  24. Fairburn, C.G. & Beglin, S. in Cognitive Behavior Therapy and Eating Disorders (ed. C.G. Fairburn) 309–313 (Guilford Press, 2008).

  25. Stunkard, A.J. & Messick, S. J. Psychosom. Res. 29, 71–83 (1985).

    Article  CAS  Google Scholar 

  26. Brainard, D.H. Spat. Vis. 10, 433–436 (1997).

    Article  CAS  Google Scholar 

  27. Croxson, M.S. & Ibbertson, H.K. J. Clin. Endocrinol. Metab. 44, 167–174 (1977).

    Article  CAS  Google Scholar 

  28. Bates, D., Maechler, M. & Bolker, B. Vol. Version: 0.999999–2. Technical Report (2011).

  29. Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2011).

  30. Schielzeth, H. & Forstmeier, W. Behav. Ecol. 20, 416–420 (2009).

    Article  Google Scholar 

  31. Venables, W.N. & Ripley, B.D. Modern applied statistics with S, 4th edition (Springer, 2002).

  32. Smith, S.M. et al. Neuroimage 23 (suppl. 1), S208–S219 (2004).

    Article  Google Scholar 

  33. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Neuroimage 17, 825–841 (2002).

    Article  Google Scholar 

  34. Smith, S.M. Hum. Brain Mapp. 17, 143–155 (2002).

    Article  Google Scholar 

  35. Andersson, J.L.R., Jenkinson, M. & Smith, S. TR07JA2: Non-linear Registration, aka Spatial Normalization (FMRIB, 2010).

  36. Nichols, T.E. & Holmes, A.P. Hum. Brain Mapp. 15, 1–25 (2002).

    Article  Google Scholar 

  37. Winkler, A.M., Ridgway, G.R., Webster, M.A., Smith, S.M. & Nichols, T.E. Neuroimage 92, 381–397 (2014).

    Article  Google Scholar 

  38. Bartra, O., McGuire, J.T. & Kable, J.W. Neuroimage 76, 412–427 (2013).

    Article  Google Scholar 

  39. Samuelson, P.A. Economica 5, 61–71 (1938).

    Article  Google Scholar 

  40. Kable, J.W. & Glimcher, P.W. Nat. Neurosci. 10, 1625–1633 (2007).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank J. Schebendach for guidance and support for the laboratory meals and nutritional analyses. This research was supported in part by the Global Foundation for Eating Disorders and the US National Institute for Mental Health (R01 MH079397, K23 MH076195).

Author information

Authors and Affiliations

Authors

Contributions

K.F., J.E.S., D.S. and B.T.W. designed the research. J.E.S. collected the data. K.F. and J.E.S. analyzed the data. K.F., J.E.S., D.S. and B.T.W. wrote the manuscript.

Corresponding authors

Correspondence to Karin Foerde or Joanna E Steinglass.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Health and Taste rating behavior.

Behavior in Health and Taste rating phases (n = 42). (a) When rating items for “Healthiness,” both groups rated the high-fat items lower (F(1,40) = 802.9, P = 4.3*10-28). AN additionally rated foods lower overall (F(1,40) = 10.10, P = 0.003). (b) When rating the items for “Tastiness,” AN rate food as less tasty (F(1,40) = 10.07, P = 0.000007), an effect that is more pronounced when rating high-fat foods (Group X Food Type interaction: F(1,40) = 5.21, P = 0.028). (c) Logistic regression of Health and Taste ratings on choice. Among the HCs, choice was more influenced by taste than by health (χ2 = 75.64, P < 0.000001), whereas AN choices were influenced by both taste and health (χ2 = 0.22, P = 0.64). (d) Multilevel linear regression of Taste on Health ratings showed that Healthiness and Tastiness were relatively independent in HC, whereas there was a significantly greater association between Health and Taste ratings in AN than in HC (z = 5.3, P = 0.02). (e) HC slowed responses when rating Healthiness of high-fat foods, whereas AN slowed responses when rating Tastiness of high-fat foods (when adjusting for overall group differences in RT; Rating phase X Food type X Group interaction F(1,40) = 11.81, P = 0.001). (f) When asked directly, most HC found Taste easier to rate, whereas most AN found Health easier to rate (proportions significantly different, χ2(1, N = 40) = 10.15, P = 0.002). Data are mean ± s.e.m. (abe); Data are fixed effects coefficients ± s.e. (cd).

Supplementary Figure 2 Need and implementation of “self-control”.

Assessing “self-control” use across participants (n = 42). (a) A conflict between Health and Taste ratings creates choice trials with opportunity for self-control (examples outlined in pink): Choosing healthy, less tasty options or not choosing unhealthy, tasty options reflects self-controlled choices. Health-Taste rating alignment leaves no opportunity for self-control (examples outlined in orange). (b) On trials with opportunity for self-control, individuals with AN exercised self-control on a greater proportion of trials relative to Controls (t(33.88) = –4.89, P = 0.000024). However, individuals with AN had significantly fewer trials with opportunity for self-control (t(40) = 3.1, P = 0.004; MHC = 27.8 ± 7.2; MAN = 19.9 ± 9.3). This was likely due to greater alignment between Healthiness and Tastiness across items in AN (See Supplementary Fig. 1d). Thus, changes in valuation according to Health and Taste in AN resulted in diminished need for self-control, suggesting an alternate strategy for making food decisions. (c) Response times when opting for or against self-controlled choices. As expected, HC slowed down when making self-controlled choices, whereas individuals with AN sped up (Group X Self-control use interaction: χ2(1, N = 41) = 4.78, P = 0.03; 1 AN participant had no valid trials for this analysis). Response times on trials where no self-control was needed are shown for comparison (orange bars). Thus, for AN, in contrast to HC, engaging “self-control” did not result in a response time cost. Note that the low number of trials involving self-control precluded fMRI data analysis of self-control trials. Such analyses depend both on trials where self-control was required and deployed as well as trials where self-control was required but not deployed. Only 16 participants (8 HC, 8 AN) had 4 or more trials in each self-control bin. Data are mean ± s.e.m.

Supplementary Figure 3 Characterizing dorsal striatum responses.

(a) To assess the specificity of the differences observed between HC and AN related to food choices, values were extracted from the cluster identified in the choice analysis (Fig. 2b). No significant differences between HC and AN were found for Health or Taste ratings in the dorsal striatum region that showed a significant difference in the Choice phase (Health: t(40) = –0.81, P = 0.42, n = 42; Taste: t(39) = –0.41, P = 0.68, n = 41). Thus, the difference between groups in the dorsal striatum was specific to the Choice phase. Note that this ROI analysis is independent as the region was identified based on activity in the Choice phase. Data are mean ± s.e.m. (b) Parametric analysis of choice ratings for low fat and high fat trials separately within the caudate cluster identified in contrast between AN and HC reported in the manuscript (Fig. 2b, n = 42). There was a significant difference between AN and HC for low fat (t(40) = –2.29, P = 0.028) but not for high fat (t(40) = –1.396, P = 0.170) trials. There were no significant differences between high and low fat trials within the HC (t(20) = -1.03, P = 0.32) or AN group (t(20) = –0.87, P = 0.40). Data are mean ± s.e.m. (c) Choice phase response bin analysis within the caudate cluster identified in contrast between AN and HC reported in the manuscript (Fig. 2b, n = 42). Data were extracted for each response bin (1-5), to further assess whether the response in the dorsal striatum increased with increasing decision strength in the Choice phase or instead reflected a binary “Yes”/”No” response to the test food over the Reference food. Data are mean ± s.e.m.

Supplementary Figure 4 Comparison of HC versus AN choice-related activity in the vmPFC.

(a) BOLD activity in regions of the vmPFC correlated with trial-by-trial choice values in both HC (left) and AN (middle) groups, with no significant difference between them (right) (FWE-corrected P < 0.05 whole-brain, cluster-forming threshold Z > 2.3, n = 42). Results from whole-brain analysis in both groups are displayed. Images and coordinates in Montreal Neurological Institute (MNI) space and radiological orientation (Right = Left). To further probe the lack of group differences within vmPFC, we performed an independent analysis using ROIs identified in previous studies of value based choice (ROIs displayed in blue): (b) Hare et al.; MNI coordinates = [3 51 3] (t(40) = 0.23, P = 0.82), and (c) Bartra et al. meta-analysis (t(40) = 0.52, P = 0.61). Data are mean ± s.e.m.

Supplementary Figure 5 Rating phase vmPFC analyses

We assessed vmPFC responses in the Health and Taste rating phases using the same region identified by Hare et al (2009); MNI coordinates = [3 51 3]. (a) There were no significant differences in BOLD response between the HC and the AN group in the Health (t(40) = 1.44, P = 0.16, n = 42) or Taste phase (t(39) = 0.26, P = 0.54, n = 41). Data are mean ± s.e.m. (b) BOLD signal during the Health and Choice phases were significantly correlated in the AN group (r = 0.44, P = 0.046); BOLD signal during Taste and Choice phases were significantly correlated in the HC group (r = 0.49, P = 0.024).

Supplementary Figure 6 Fronto-striatal connectivity on low-fat and high-fat food trials in Anorexia Nervosa.

(a) Fronto-striatal connectivity for low-fat foods was significantly negatively correlated with food intake in AN (r(14) = –0.51, P = 0.04, n = 16), (b) whereas connectivity for high-fat foods showed a trend towards a positive correlation with real food intake (r(14) = 0.44, P = 0.09, n = 16). Robust regressions including the participants who binge-ate yielded the same pattern of associations between eating behavior and connectivity on low-fat food trials (t(17) = 2.04, P = 0.057, n = 19) and high-fat food trials (t(17) = –1.92, P = 0.072, n = 19).

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Foerde, K., Steinglass, J., Shohamy, D. et al. Neural mechanisms supporting maladaptive food choices in anorexia nervosa. Nat Neurosci 18, 1571–1573 (2015). https://doi.org/10.1038/nn.4136

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