Multivariate neural signatures for health neuroscience: assessing spontaneous regulation during food choice
Danielle Cosme, Dagmar Zeithamova, Eric Stice, Elliot T Berkman, Danielle Cosme, Dagmar Zeithamova, Eric Stice, Elliot T Berkman
Abstract
Establishing links between neural systems and health can be challenging since there is not a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation (CR) using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring CR ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded CR from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in CR despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience.
Keywords: craving regulation; food valuation; health neuroscience; multivariate fMRI; neural signature.
© The Author(s) 2020. Published by Oxford University Press.
Figures
References
- Berkman E.T., Falk E.B. (2013). Beyond brain mapping: using neural measures to predict real-world outcomes. Current Directions in Psychological Science, 22(1), 45–50. doi: 10.1177/0963721412469394.
- Boswell R.G., Kober H. (2016). Food cue reactivity and craving predict eating and weight gain: a meta-analytic review: food cue reactivity and craving meta-analysis. Obesity Reviews, 17(2), 159–77. doi: 10.1111/obr.12354.
- Boswell R.G., Sun W., Suzuki S., Kober H. (2018). Training in cognitive strategies reduces eating and improves food choice. Proceedings of the National Academy of Sciences, 115(48), E11238–47. doi: 10.1073/pnas.1717092115.
- Burger K.S., Stice E. (2012). Frequent ice cream consumption is associated with reduced striatal response to receipt of an ice cream–based milkshake. The American Journal of Clinical Nutrition, 95(4), 810–7. doi: 10.3945/ajcn.111.027003.
- Bzdok D., Ioannidis J.P.A. (2019). Exploration, inference, and prediction in neuroscience and biomedicine. Trends in Neurosciences, 42(4), 251–62. doi: 10.1016/j.tins.2019.02.001.
- Chang L.J., Gianaros P.J., Manuck S.B., Krishnan A., Wager T.D. (2015). A sensitive and specific neural signature for picture-induced negative affect. PLoS Biology, 13(6), e1002180. doi: 10.1371/journal.pbio.1002180.
- Corbin N., Todd N., Friston K.J., Callaghan M.F. (2018). Accurate modeling of temporal correlations in rapidly sampled fMRI time series. Human Brain Mapping, 39(10), 3884–97. doi: 10.1002/hbm.24218.
- Cosme D., Flournoy J.C., Vijayakumar N. (2018). auto-motion-fmriprep: A tool for automated assessment of motion artifacts (Version v1.0) [Computer software]. 10.5281/zenodo.1412131.
- Cosme D., Ludwig R.M., Berkman E.T. (2019). Comparing two neurocognitive models of self-control during dietary decisions. Social Cognitive and Affective Neuroscience, 14(9), 957–966. doi: 10.1093/scan/nsz068.
- Cumming G. (2014). The new statistics: why and how. Psychological Science, 25(1), 7–29. doi: 10.1177/0956797613504966.
- Demos K.E., Heatherton T.F., Kelley W.M. (2012). Individual differences in nucleus accumbens activity to food and sexual images predict weight gain and sexual behavior. Journal of Neuroscience, 32(16), 5549–52. doi: 10.1523/JNEUROSCI.5958-11.2012.
- Doré B.P., Tompson S.H., O’Donnell M.B., An L.C., Strecher V., Falk E.B. (2019). Neural mechanisms of emotion regulation moderate the predictive value of affective and value-related brain responses to persuasive messages. Journal of Neuroscience, 39(7), 1293–300. doi: 10.1523/JNEUROSCI.1651-18.2018.
- Doré B.P., Weber J., Ochsner K.N. (2017). Neural predictors of decisions to cognitively control emotion. The Journal of Neuroscience, 37(10), 2580–8. doi: 10.1523/JNEUROSCI.2526-16.2016.
- Esteban O., Markiewicz C.J., Blair R.W., et al. (2019). fMRIPrep: a robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111. doi: 10.1038/s41592-018-0235-4.
- Falk E.B., Berkman E.T., Whalen D., Lieberman M.D. (2011). Neural activity during health messaging predicts reductions in smoking above and beyond self-report. Health Psychology, 30(2), 177. doi: 10.1037/a0022259.
- Fischl B. (2012). FreeSurfer. NeuroImage, 62(2), 774–81. doi: 10.1016/j.neuroimage.2012.01.021.
- Giuliani N.R., Mann T., Tomiyama A.J., Berkman E.T. (2014). Neural systems underlying the reappraisal of personally craved foods. Journal of Cognitive Neuroscience, 26(7), 1390–402. doi: 10.1162/jocn_a_00563.
- Giuliani N.R., Pfeifer J.H. (2015). Age-related changes in reappraisal of appetitive cravings during adolescence. NeuroImage, 108, 173–81. doi: 10.1016/j.neuroimage.2014.12.037.
- Giuliani N.R., Tomiyama A.J., Mann T., Berkman E.T. (2015). Prediction of daily food intake as a function of measurement modality and restriction status. Psychosomatic Medicine, 77(5), 583–90. doi: 10.1097/PSY.0000000000000187.
- Gross J.J. (1998). Antecedent-and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74(1), 224.
- Hall P.A., Bickel W.K., Erickson K.I., Wagner D.D. (2018). Neuroimaging, neuromodulation, and population health: the neuroscience of chronic disease prevention. Annals of the New York Academy of Sciences, 1428(1), 240–56. doi: 10.1111/nyas.13868.
- Hutcherson C.A., Plassmann H., Gross J.J., Rangel A. (2012). Cognitive regulation during decision making shifts behavioral control between ventromedial and dorsolateral prefrontal value systems. The Journal of Neuroscience, 32(39), 13543–54. doi: 10.1523/JNEUROSCI.6387-11.2012.
- Kober H., Mende-Siedlecki P., Kross E.F., et al. (2010). Prefrontal–striatal pathway underlies cognitive craving regulation. Proceedings of the National Academy of Sciences, 107(33), 14811–6. doi: 10.1073/pnas.1007779107.
- 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), 1337–44.
- Poldrack R.A. (2010). Mapping mental function to brain structure: how can cognitive neuroimaging succeed? Perspectives on Psychological Science: A Journal of the Association for Psychological Science, 5(5), 753–61. doi: 10.1177/1745691610388777.
- Poldrack R.A. (2011). Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron, 72(5), 692–7. doi: 10.1016/j.neuron.2011.11.001.
- Rapuano K.M., Huckins J.F., Sargent J.D., Heatherton T.F., Kelley W.M. (2016). Individual differences in reward and somatosensory-motor brain regions correlate with adiposity in adolescents. Cerebral Cortex, 26(6), 2602–11. doi: 10.1093/cercor/bhv097.
- Rissman J., Gazzaley A., D’Esposito M. (2004). Measuring functional connectivity during distinct stages of a cognitive task. NeuroImage, 23(2), 752–63. doi: 10.1016/j.neuroimage.2004.06.035.
- Shahane A.D., Lopez R.B., Denny B.T. (2019). Implicit reappraisal as an emotional buffer: Reappraisal-related neural activity moderates the relationship between inattention and perceived stress during exposure to negative stimuli. Cognitive, Affective, & Behavioral Neuroscience, 19(2), 355–365. doi: 10.3758/s13415-018-00676-x.
- Simonsohn U., Simmons J.P., Nelson L.D. (2015). Specification curve: descriptive and inferential statistics on all reasonable specifications. SSRN Electronic Journal. doi: 10.2139/ssrn.2694998.
- Stice E., Yokum S., Burger K., Rohde P., Shaw H., Gau J.M. (2015). A pilot randomized trial of a cognitive reappraisal obesity prevention program. Physiology & Behavior, 0, 124–32. doi: 10.1016/j.physbeh.2014.10.022.
- Wager T.D., Atlas L.Y., Lindquist M.A., Roy M., Woo C.-W., Kross E. (2013). An fMRI-based neurologic signature of physical pain. New England Journal of Medicine, 368(15), 1388–97. doi: 10.1056/NEJMoa1204471.
- Westfall J., Nichols T., Yarkoni T. (2016). Fixing the stimulus-as-fixed-effect fallacy in task fMRI (No. biorxiv;077131v1). doi: 10.1101/077131
- Woo C.-W., Chang L.J., Lindquist M.A., Wager T.D. (2017). Building better biomarkers: brain models in translational neuroimaging. Nature Neuroscience, 20(3), 365–77. doi: 10.1038/nn.4478.
- Yarkoni T., Westfall J. (2017). Choosing prediction over explanation in psychology: lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–22. doi: 10.1177/1745691617693393.
- Yokum S., Stice E. (2013). Cognitive regulation of food craving: effects of three cognitive reappraisal strategies on neural response to palatable foods. International Journal of Obesity, 37(12), 1565–70. doi: 10.1038/ijo.2013.39.
Source: PubMed