Acute social isolation evokes midbrain craving responses similar to hunger

In the paper

“whereas social cues were numerically but not significantly higher after isolation than baseline and showed a trend to be lower after fasting (b = 0.01, t = −1.8, 95% CI = (−0.11,−0.004), P = 0.07”

In the paper

“Self-reported food craving on fasting days and social contact craving on isolation days were both higher than on the baseline day (Food_Craving_CIC: t(39) = 7.94, P < 0.001, two tailed; Social_Craving_CIC: t(39) = 4.15, P < 0.001, two tailed).”

In the paper

“Midbrain regions showed selective responses to food cues after fasting and to social cues after isolation.”

In the paper

“self-reported cravings for food and social interaction were moderately correlated at baseline (r = 0.48), but less correlated after either need was deprived (mean r = 0.14, z = 1.64, P = 0.05).”

In the paper

“An exploratory whole-brain, random-effects analysis yielded converging results: selective responses to food craving in the NAcc, ACC, periaqueductal gray and amygdala, and selective responses to social craving in the Ca, OFC and dorsomedial prefrontal cortex”

In the paper

“Yet despite our restricted sample, we observed substantial variability between individuals at baseline, in both self-reported social craving and in SN/VTA response to social cues.”

In the paper

“In all, our finding of a more selective SN/VTA response to social cues after isolation, as well as to food cues after fasting, fits the intuitive idea that positive social interactions are a basic human need, and acute loneliness is an aversive state that motivates people to repair what is lacking, similar to hunger.”

In the paper

“However, because the measures were highly correlated (r(39) = 0.58, P < 0.001), we z-transformed and averaged the two measures for each participant.”

In the paper

“As the data for food craving were truncated at the upper limit of 10 (Fig. 3c)”

In the paper

“In the unsmoothed data, we found no above-chance decoding (α = 0.001) of social cues from flowers after isolation (mean accuracy = 0.521, bootstrapped CI = (0.503,0.542), P = 0.041) or after baseline (mean accuracy = 0.526, bootstrapped CI = (0.501,0.546), P = 0.021).”

In the paper

“In addition, in unsmoothed data, the classifier was also unable to decode food cues from flowers after isolation and baseline (isolation: mean accuracy = 0.522, bootstrapped CI = (0.500,0.544), P = 0.021; baseline: mean accuracy = 0.525, bootstrapped CI = (0.501,0.557), P = 0.022).”

In the paper

“In the present study, we found that omission of smoothing showed slightly lower mean accuracy across subjects (mean accuracy = 0.542), but still resulted in successful above chance (50%) decoding of food cues from flower images after fasting (P = 0.002, 95% CI = 0.513–0.570).”

In the paper

“we also ran the classification analyses on unsmoothed data and found that we were not able to decode stimulus or motivational state across sessions in the unsmoothed data”

In the paper

“A second limitation is that we studied a small sample (n = 40) of healthy, well-connected young adults, mostly students, in one cultural context.”

In the paper

“Note, however, that, also in domains such as drug addiction and hunger, researchers have struggled to establish a gold-standard behavioral metric of subjective craving with better external validity than self-report45.”

What the sources say

• Not checked yetcites [45] Sayette, M. A. et al. The measurement of drug craving. Addiction 95, S189–S210 (2000). source

In the paper

“These results suggest that there is a reliable individual difference in craving responses after deprivation, shared across both fasting and isolation, consistent with our observation of a common neural response to both modes of deprivation.”

In the paper

“Chronic loneliness could thus be a consequence, rather than a cause, of general low responsiveness in the SN/VTA.”

In the paper

“Thus, although the responses in SN/VTA were similar and overlapping for food craving and social craving, responses in the striatum were dissociable.”

In the paper

“Thus, at baseline participants’ motivation may be spread across multiple sources of reward, and specific acute deprivation may serve to narrow and focus the brain’s motivational responses to the deprived target, rather than to enhance it.”

In the paper

“First, we measured craving using passive viewing of craving-related cues and self-report measures of craving, rather than a direct test of motivation, such as participants’ willingness to expend effort or money to fulfill a need.”

In the paper

“Despite differences in the duration and setting of social isolation, and in the anatomy of DA midbrain structures, both humans and mice seem to show midbrain craving responses for social interaction, as well as for food.”

In the paper

“Fasting was confirmed through self-report on arrival.”

In the paper

“We did not measure people’s social behaviors at baseline (for example, number and quality of social interactions) or other traits broadly relevant to social interaction (for example, trait extroversion, depression, anxiety).”

In the paper

“In all, our finding of a more selective SN/VTA response to social cues after isolation, as well as to food cues after fasting, fits the intuitive idea that positive social interactions are a basic human need, and acute loneliness is an aversive state that motivates people to repair what is lacking, similar to hunger.”

In the paper

“Between participants, the order of sessions was counterbalanced; each participant was pseudo-randomly assigned to one of the possible orders of the three different sessions, with the restriction that all six possible sequences were approximately equally likely in the full sample.”

In the paper

“An initial power analysis in G*Power 3.0 (ref.51) targeted on the detection of medium effects (d = 0.5, α = 0.05 and 1 − β = 0.80) suggested a sample size of n = 34.”

What the sources say

• Not checked yetcites [51] Faul, F., Erdfelder, E., Lang, A. G. & Buchner, A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39, 175–191 (2007). source

In the paper

“We therefore recruited 42 participants to account for potential attrition or exclusion for MRI data quality; 2 participants were unable to complete all experimental sessions and so were dropped from analysis, leaving 40 complete datasets.”

In the paper

“The targeted-effect size was chosen based on findings from studies employing CIC paradigms for drug craving52, food cravings25,37,53,54 and internet gaming craving27, which report medium-to-large effect sizes in cue reactivity52.”

What the sources say

• Not checked yetcites [52] Carter, B. L. & Tiffany, S. T. Meta-analysis of cue-reactivity in addiction research. Addiction 94, 327–340 (1999). source
• Not checked yetcites [25] Malik, S., McGlone, F., Bedrossian, D. & Dagher, A. Ghrelin modulates brain activity in areas that control appetitive behavior. Cell Metab. 7, 400–409 (2008). source
• Not checked yetcites [37] Goldstone, A. P. et al. Fasting biases brain reward systems towards high-calorie foods. Eur. J. Neurosci. 30, 1625–1635 (2009). source
• Not checked yetcites [53] Stice, E., Burger, K. & Yokum, S. Caloric deprivation increases responsivity of attention and reward brain regions to intake, anticipated intake, and images of palatable foods. Neuroimage 67, 322–330 (2013). source
• Not checked yetcites [54] Stevenson, R. J., Francis, H. M., Attuquayefio, T. & Ockert, C. Explicit wanting and liking for palatable snacks are differentially affected by change in physiological state, and differentially related to salivation and hunger. Physiol. Behav. 182, 101–106 (2017). source
• Not checked yetcites [27] Zhang, J. T. et al. Effects of craving behavioral intervention on neural substrates of cue-induced craving in Internet gaming disorder. Neuroimage Clin. 12, 591–599 (2016). source

In the paper

“The scanning parameters were extensively piloted (n = 11) using the functional localizer task (see “Functional localizer task”) and the parameters showing the best signal-to-noise ratio were selected for the study.”

In the paper

“Data collection was not performed blind to the conditions of the experiments.”

In the paper

“Any run containing a framewise displacement >0.4 mm on >25% of the total frames was excluded from additional analyses.”

In the paper

“A potential limitation of this approach is that it was targeting voxels responding to secondary reinforcers (that is, money) rather than primary reinforcers (such as food and social contact).”

In the paper

“Conditioned stimuli are somewhat less effective than primary rewards in terms of response magnitude and fractions of neurons activated67.”

What the sources say

• Not checked yetcites [67] Schultz, W. Getting formal with dopamine and reward. Neuron 36, 241–263 (2002). source

In the paper

“In addition, our functional localization method was complementary to an anatomical ROI, which would not be affected by this issue.”

In the paper

“In animal models, dopamine neurons in the midbrain exhibit burst firing in response to both primary reinforcers and conditioned stimuli once the conditioning has been established (for example, see refs.65,66).”

What the sources say

• Not checked yetcites [65] Day, J. J., Roitman, M. F., Wightman, R. M. & Carelli, R. M. Associative learning mediates dynamic shifts in dopamine signaling in the nucleus accumbens. Nat. Neurosci. 10, 1020–1028 (2007). source
• Not checked yetcites [66] Overton, P. & Clark, D. Burst firing in midbrain dopaminergic neurons. Brain Res. Rev. 25, 312–334 (1997). source

In the paper

“As midbrain DA neurons in the SN/VTA respond to both novelty and reward62–64, we contrasted new and rewarding stimuli against familiar and nonrewarding stimuli.”

What the sources say

• Not checked yetcites [62] Krebs, R. M., Heipertz, D., Schuetze, H. & Duzel, E. Novelty increases the mesolimbic functional connectivity of the substantia nigra/ventral tegmental area (SN/VTA) during reward anticipation: evidence from high-resolution fMRI. Neuroimage 58, 647–655 (2011). source
• Not checked yetcites [63] Bunzeck, N. & Duzel, E. Absolute coding of stimulus novelty in the human substantia nigra/VTA. Neuron 51, 369–379 (2006). source
• Not checked yetcites [64] Schultz, W. Neural coding of basic reward terms of animal learning theory, game theory, microeconomics and behavioural ecology. Curr. Opin. Neurobiol. 14, 139–147 (2004). source

In the paper

“The angle of the slices was approximately 20° away from the plane of the standard anterior commissure–posterior commissure to avoid placing the SN/VTA and the sphenoid sinus in the same slice plane. This reduced geometric distortion to the point that standard distortion correction methods could be applied28.”

What the sources say

• Not checked yetcites [28] Duzel, E. et al. Midbrain fMRI: applications, limitations and challenges. in fMRI: From Nuclear Spins to Brain Functions (eds Uludağ, K., Uğurbil, K. & Berliner, L.) 581–609 (Springer, 2015). source

In the paper

“Three participants missed filling out the first or last round of the online questionnaire during fasting, so statistical analyses involving this questionnaire were conducted with a sample size of n = 37.”

In the paper

“Importantly, both food and social cues would be similarly affected, so our key claim of similarity between these responses would not be undermined.”

In the paper

“Thus, by localizing the SN/VTA based on activity in response to anticipated financial rewards, we may have identified only a subset of voxels that respond to primary rewards.”

In the paper

“However, our preregistered hypotheses focus on the anatomical localization strategy, so we treat analyses of the functionally localized regions as exploratory.”

In the paper

“Exclusion criteria were pre-established in our preregistration (https://osf.io/cwg9e). We excluded three runs of fMRI data from the overall sample (from two participants from the CIC task) based on the following criterion: any run containing a framewise displacement >0.4 mm on >25% of the total frames will be excluded from additional analyses.”

In the paper

“The stimuli were images of strangers, rather than images of the participant’s own friends and family, to (1) match the food and control images for novelty, because SN/VTA activity is sensitive to novelty62, (2) match image quality across conditions and participants, and (3) avoid unmeasured variability in the quality or current status of participants’ relationships with specific individuals.”

What the sources say

• Not checked yetcites [62] Krebs, R. M., Heipertz, D., Schuetze, H. & Duzel, E. Novelty increases the mesolimbic functional connectivity of the substantia nigra/ventral tegmental area (SN/VTA) during reward anticipation: evidence from high-resolution fMRI. Neuroimage 58, 647–655 (2011). source

In the paper

“We note that this method of correction for multiple comparisons is probably biased toward false positives97,98.”

What the sources say

• Not checked yetcites [97] Eklund, A., Knutsson, H. & Nichols, T. E. Cluster failure revisited: impact of first level design and physiological noise on cluster false positive rates. Hum. Brain Mapp. 40, 2017–2032 (2019). source
• Not checked yetcites [98] Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl Acad. Sci. USA 113, 7900–7905 (2016). source

In the paper

“This nonparametric approach does not depend on assumptions about the distribution of classification accuracies94,95.”

What the sources say

• Not checked yetcites Stelzer et al.94 Stelzer, J., Chen, Y. & Turner, R. Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): random permutations and cluster size control. Neuroimage 65, 69–82 (2013). source
• Not checked yetcites [94] Stelzer, J., Chen, Y. & Turner, R. Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): random permutations and cluster size control. Neuroimage 65, 69–82 (2013). source
• Not checked yetcites [95] Allefeld, C., Görgen, K. & Haynes, J.-D. Valid population inference for information-based imaging: from the second-level t-test to prevalence inference. Neuroimage 141, 378–392 (2016). source

In the paper

“This measure was exactly comparable across sessions and simultaneous with the fMRI data acquisition.”

In the paper

“We corrected for multiple comparisons using whole-brain family-wise error correction.”

In the paper

“Although smoothing has been shown not to substantially affect information in multivariate data91–93”

What the sources say

• Not checked yetcites [91] Hendriks, M. H. A., Daniels, N., Pegado, F. & Op de Beeck, H. P. The effect of spatial smoothing on representational similarity in a simple motor paradigm. Front. Neurol. 8, 222 (2017). source
• Not checked yetcites [92] Kamitani, Y. & Sawahata, Y. Spatial smoothing hurts localization but not information: pitfalls for brain mappers. Neuroimage 49, 1949–1952 (2010). source
• Not checked yetcites [93] Op de Beeck, H. P. Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses? Neuroimage 49, 1943–1948 (2010). source

In the paper

“but this measure provides the most direct measure of the effect of deprivation because it can be compared with the self-report at the start of each session.”

In the paper

“These analyses were exploratory and statistical inference was performed using a threshold of P < 0.05 corrected for multiple comparisons over the whole brain, using the Gaussian random fields approach at cluster level with a voxel-level intensity threshold of P < 0.001 (ref.96).”

What the sources say

• Not checked yetcites ref.96

In the paper

“Following the recommendation of Barr et al.87, we used maximal models, including participants as a random effect with both random intercepts and random slopes.”

What the sources say

• Not checked yetcites [87] Barr, D. J., Levy, R., Scheepers, C. & Tily, H J. Random effects structure for confirmatory hypothesis testing: keep it maximal. J. Memory Lang. 68, 255–278 (2013). source

In the paper

“We modeled these effects in the anatomically defined SN/VTA (preregistered analysis) and in the functionally defined ROI of voxels maximally sensitive to reward and novelty (exploratory analysis).”