responses evoked by food and social cues after participants (n=40) experienced ten hours of mandated fasting or total social isolation. After isolation, people felt lonely and craved social interaction. Midbrain regions showed selective activation to food cues after fasting and to social cues after isolation; these responses were correlated with self-reported craving. By contrast, striatal and cortical regions differentiated between craving food versus social interaction. Across 25 deprivation sessions, we find that deprivation narrows and focuses the brain's motivational responses to the deprived target. Our results support the intuitive idea that acute isolation causes social craving, similar to the way fasting causes hunger.
How are people affected by a period of forced social isolation? Chronic social isolation and loneliness are associated with lower physical 1,2 and mental 2-4 health, but little is known about the consequences of acute mandatory isolation. Positive social interactions in and of themselves may be basic human needs, analogous to other basic needs like food consumption or sleep 5, 6 . If so, the absence of positive social interaction may create a want, or "craving", that motivates behavior to 5 repair what is lacking 5 . Cues associated with positive social interaction (e.g., smiling faces) activate neural reward systems 7, for review . However, research on the neural representation of unmet human social needs is scarce 8 .
In social animals, social interactions act as primary rewards [9] [10] [11] : they are inherently pleasurable 10 and motivate behavior in the absence of any other reward. Extended periods of isolation, especially during development, can dramatically disrupt behavior and brain function [12] [13] [14] . Even a brief acute period of social isolation causes an aversive, 'loneliness-like' brain state in adult mice, causing the mice to seek social interaction 15 which is mediated specifically by dopaminergic midbrain neurons 16 , similar to other kinds of craving 17, for review . 15 However, the homology to human loneliness has been disputed 8 , and it is not possible to assess whether a mouse subjectively feels lonely when isolated. Would acute isolation evoke a similar response in humans? The intuitive idea that depriving social needs evokes social craving, analogous to the way fasting evokes food craving and is mediated by similar dopaminergic 20 midbrain regions, has never been directly tested in humans. The few prior studies investigating the correlation between self-reported chronic loneliness and brain responses to social stimuli yield contradictory findings [18] [19] [20] and are limited by the ambiguity of observed correlations: if brain responses do differ, are the differences antecedents or effects of loneliness? 25 To address these questions, we experimentally induced social isolation experimentally in a within-subject design. 40 healthy socially-connected young adults (ages 18-40, 27 women) underwent 10 hours of social isolation and an fMRI scan with a cue-induced craving paradigm.
Each participant also underwent 10 hours of food fasting and a subsequent MRI scan. Figure 1 shows an overview of the experimental procedures (see Methods for details). All predictions and 30 methods were preregistered on the Open Science Framework (https://osf.io/cwg9e/). was counterbalanced across participants) and subsequently an MRI scan with the cue-induced craving task. On the 5 baseline day, participants also underwent a functional localizer task. Cue-induced craving task: Participants saw cues for social contact, food and control cues depicting flowers. After each block of cues (showing 3 images), participants rated their self-reported social craving (after social blocks), food craving (after food blocks) and how much they liked the flower pictures (after control blocks). Functional localizer: Participants memorized a set of 5 images prior to the scan (4 different sets of images were counterbalanced across participants). Immediately before 10 the localizer task, participants were shown the memorized pictures again. During the task, participants saw either one of the memorized pictures or a novel picture indicating whether they would be able to win money or not.
All participants were within a healthy weight range (BMI mean (standard deviation(SD))= 22.8(2.2)), reported frequent social interactions (monthly interactions, mean=49.1(31.7); 15 minimum=10) and close relationships (number of close relationships, mean=12.3(5.1); minimum=3). Participants reported relatively 21 low levels of pre-existing loneliness (UCLA loneliness-scale, mean=33.2(6.3), maximum=47 out of 80).
Could we experimentally induce the subjective experience of social isolation in human 5 participants? Human loneliness is not a simple product of objective isolation: people can be alone without feeling lonely, or feel lonely even in a crowd 22 . Moreover, experimentally induced isolation would be brief, relative to the human lifespan, and for ethical reasons, human participants (unlike rodents) would be able to predict when the isolation would end. In all, the first challenge of this research was to develop an experimental induction of objective isolation 10 that created the subjective experience of unmet social needs in human participants. To address this challenge, we had socially-connected healthy human adults spend ten hours (9 am to 7 pm) alone, with no social interaction and no other social stimulation (e.g. social media, email, fiction). We used self-report questionnaires to assess people's resulting subjective experience of loneliness and social craving. 15 After ten hours of social isolation, participants reported substantially increased social craving (t(39)=5.00, p<0.001), loneliness (t(39)=5.17, p<0.001), discomfort (t(39)=5.57, p<0.001) and dislike of isolation t(39)=4.13, p<0.001), and decreased happiness (t(39)=-4.21, p<0.001) compared to when they started isolation. Thirty-six out of forty individual participants reported 20 feeling more lonely after isolation. As expected, after ten hours of food fasting participants reported increased food craving (t(36)=17. 40, p<0 .001), hunger (t(36)=23.90, p<0.001), discomfort (t(36)=13.56, p<0.001) and dislike of fasting (t(36)=6.28, p<0.001), and decreased happiness (t(36)=-3.05, p=0.004) compared to when they started fasting. Thus, both forms of abstinence evoked craving for the specifically deprived need, along with general discomfort and 25 decreased happiness. However, we note that social craving after isolation was more variable across participants than food craving after fasting (mean(SD) of craving ratings, after 10 hours: (left; n = 37) and in comparison to ratings of social craving during the cue-induced craving task (right; n =40). The lower panel shows changes in self-reported social craving over time during isolation (left; n = 40) and in comparison to ratings of food craving during the cue-induced craving task (right; n = 40). The boxplots in both panels indicate 5 the median (dark center line), the interquartile range (IQR; box) and the 1.5 IQR minima and maxima (whiskers).
Datapoints outside the whiskers are shown as individual data points.
In primates, aversive motivation (i.e., a negative state such as hunger or pain that motivates 10 behavior to relieve the state 23 ) is represented in the substantia nigra pars compacta and ventral tegmental area (SN/VTA 24 ), and SN/VTA is activated by craving for food and for drugs of addiction [25] [26] [27] . In the substantia nigra in particular, ~70% of the neurons are dopaminergic, so fMRI signals in this brain area likely mainly reflect dopaminergic (DA) neuron activity 28 . We therefore hypothesized that acute isolation in humans might produce a social craving response in SN/VTA. Neuroimaging of the SN/VTA poses a technical challenge, though, because it is a small structure, adjacent to the sphenoid sinus (a large, air-filled cavity located anterior to the brainstem) and is therefore prone to distortions and signal loss 28 . To address this challenge, we optimized MRI image acquisition parameters, and used a newly developed atlas (see Methods section for details) to identify SN/VTA in individual participants' brains. We also included an 5 independent functional localizer task to identify voxels in each participant's midbrain that are maximally sensitive to expected reward and novelty, consistent with DA activity 29 .
To measure dopaminergic midbrain responses to food cues and social cues, we developed a cueinduced craving (CIC) paradigm, in which participants viewed pictures of their favorite social 10 activities, favorite foods, and a pleasant control (flowers). We included favorite foods so that we could compare social craving, within participants, to a well-established neural craving response, evoked by viewing food cues after several hours of fasting 26, for review . In the anatomically defined SN/VTA (Figure 3a ), responses to food cues were higher after fasting than after isolation (b=0.06, t=3.1, 95%CI=[0.0.02,0.09], p=0.002), but responses to social cues were not 15 significantly higher after isolation than after fasting (b=0.006, t=0.7, 95%CI=[-0.01,0.03], p=0.50; for full results of the model including all main effects, see Table S1 in the Supplementary Materials (SM)). In the midbrain functional ROI (voxels maximally sensitive to reward and novelty, Figure 3b ), responses to food cues were higher after fasting than after isolation (b=0.03, t=3.3, 95%CI=[0.01,0.05], p=.001), and responses to social cues were higher 20 after isolation than after fasting (b=0.03, t=2.5, 95%CI=[0.006,0.05], p=0.01; see Table S2 in the SM for full results).
We also compared SN/VTA responses to food and social cues following fasting and isolation to responses to the same cue on the baseline day (when participants' food intake and social 25 interactions were not controlled or measured). Unexpectedly, when compared to the baseline day, deprivation led to a decreased response to the non-deprived cue, rather than an enhanced response to the deprived cue (see Figures 3 and S2 ). In the anatomically defined SN/VTA, responses to food cues were numerically but not significantly higher after fasting than baseline but significantly lower after isolation than baseline (b=-0.05, t=-2.1, 95%CI [-0.11,-0.003], 30 p=0.038); responses to social cues did not significantly differ from responses at baseline for any of the sessions. In the midbrain functional ROI, responses to food cues were numerically but not significantly higher after fasting than and significantly lower after isolation than baseline (b=-0.06, t=-2.4, 95%CI [-0.12,-0.01], p=0.02); while 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; see Tables S3 and S4 in the SM for full results) 5 SN/VTA activity was correlated with self-reported craving, for both food and social cues.
However, this correlation was significant for different measures in the two conditions. We measured self-reported craving in two ways: craving ratings during the task directly in response to the craving cues (Food_Craving_CIC / Social_Craving_CIC) and craving ratings on the questionnaire before participants went into the scanner (Food_Craving_Q / Social_Craving_Q). 10 The two craving measures (Craving_CIC and Craving_Q) were correlated across participants in both conditions (food r(36)=0.52; p<0.001; social: r(38)=0.30; p=0.030); see Methods for details). We consider the two self-report measures to be estimates of the same hypothesized psychological process, and report results as significant after Bonferroni correction for two tests.
After fasting, in the anatomical SN/VTA, the response to food cues (versus flowers) was 15 positively correlated with the participant's self-reported food craving measured during the cueinduced craving task (Food_Craving_CIC: r(38)=0.31; p=0.025; Figure 3c ), but not with food craving on the final questionnaire (Food_Craving_Q, r(36)=0.05; p=0.375). Because the data for food craving measured during the cue-induced craving task is truncated at the upper limit of 10, we also analyzed the correlation using a truncated regression model (see Methods for details): 20 SN/VTA activity in response to food cues remained associated with higher cravings ratings during the task (Food_Craving_CIC: b=0.04, t=2.10, p=0.038). After isolation, the SN/VTA response to social cues (versus flowers) was positively correlated with self-reported social craving on the final questionnaire (Social_Craving_Q, r(38)=0.36; p=0.011; Figure 3c ); but not with social craving measured during the cue-induced craving task (Social_Craving_CIC: in the anatomical SN/VTA were higher for food after fasting compared to isolation. b: Responses in the midbrain functional ROI were higher for food after fasting (compared to isolation) and for social cues after isolation 10 (compared to fasting). c: In the anatomical SN/VTA, responses to food cues were correlated with craving reported in the CIC task, following fasting (upper panel) and responses to social cues were correlated with craving reported on the final Questionnaire, following isolation (lower panel).
As a direct test of the similarity of activity patterns for food cues after fasting, and social cues 15 after isolation, we implemented a multivoxel pattern analysis (MVPA). We trained a classifier on the pattern of activity in the SN/VTA after fasting to food cues and flower images. This trained classifier then generalized to above chance decoding (α=0.001; see Methods section for details and Figure 4 for an illustration of our multivariate analysis approach) of social cues from flower images after isolation (mean accuracy=0.542, bootstrapped-CI=[0.505,0.581], p<0.001) but did ). However, it is important to note that the classification accuracy for social cues did not differ between the isolation and baseline sessions.
In addition, the classifier was able to decode food from flower images after isolation and baseline (isolation: mean accuracy=0.540, bootstrapped-CI=[0.494,0.584], p<0.001; baseline: mean 5 accuracy=0.545, bootstrapped-CI=[0.509,0.594], p<0.001). Given that the classification algorithm was always used to distinguish between a target stimulus and flowers, one might worry that the generalization is simply due to learning about flowers. We addressed this concern using a representational similarity analysis that directly compared food and social cues in deprived or undeprived states. 10 The pattern of SN/VTA activity in response to social cues on the isolation day was more similar to the pattern of food cues on the fasting day, than to food cues on the baseline day (mean fisher- This classifier was then tested on patterns of activity in the same participant in the other two sessions. (b) For comparison, we generated null distributions using permutation and bootstrapping procedures: first, we permuted the 5 labels within runs for each participant's training data, and then tested the permuted classifier on each testing dataset.
We created a null distribution by randomly sampling one of the permuted accuracies for each participant and then averaging the accuracy values across subjects. This procedure was repeated 10 5 times to generate the group level null distributions. (c) Null distributions for each testing data set. The dotted red line is showing the true accuracy value (i.e., classification accuracy from the real classifier) for each testing dataset. The classifier generalized to (i.e. 10 successfully decoded above chance) food cues (vs control cues) on both isolation and baseline days; and to social cues (vs control cues) on the isolation day, but not on the baseline day. (D) We directly compared the spatial pattern of activity for deprived and non-deprived cues. Note that these results show fisher-transformed dissimilarity, a measure of representational distance, so lower numbers indicate a more similar spatial pattern. The violins illustrate the distribution of the data, the white dots indicate the median, the bold dark grey vertical line the interquartile range (IQR) and the thin grey lines the 1.5 IQR minima and maxima. Social cues after isolation were more similar to food cues after fasting than to food cues at baseline. Food cues after fasting were (trending) more similar to social cues after isolation than food cues after isolation. Both (C) and (D) show that the pattern of activity in SN/VTA is determined not only by the category of visual stimulus, but by the motivational salience of the category, with craved 5 cues evoking a similar pattern whether what is craved is food or social interaction.
In sum, these results suggest that across all participants, SN/VTA shows an increased response to social cues after objective social isolation compared to after fasting, with a spatial pattern that is similar to the response to food cues when hungry. The magnitude of this response was variable 10 across participants, and larger in those who reported more social craving after the acute isolation period. We predicted that individual variability in response to objective isolation might reflect pre-existing differences in participants' social network size and/or chronic loneliness. Consistent with this prediction, participants with higher levels of chronic loneliness during the initial screening reported less craving for social contact in response to the social cues in the CIC task 15 (Social_Craving_CIC: r(38)=-0.37; p=0.020) and somewhat less craving after 10 hours of isolation, on the online questionnaire (Social_Craving_Q: r(38)=-0.30; p=0.059). People with higher pre-existing chronic loneliness also showed a muted response in SN/VTA to social cues after acute isolation (r(38)=-0.33; p=0.036). Individual differences in social network size did not predict either self-reported or neural responses to acute social isolation (all p-values>=0.33). We 20 explored whether pre-existing loneliness was associated with different responses to food cues after fasting and find that while loneliness did not affect self-reported food craving (p=0.431), higher loneliness was associated with a trend towards lower post-fasting SN/VTA responses to food cues (r(38)=-0.30; p=0.062). 25 
In our pre-registration, we planned to test whether the magnitude of SN/VTA response to a specific deprived cue (food or social cues) was correlated with self-reported craving for that cue. As reported above, these predicted correlations were observed. In addition to these pre-registered analyses, as prompted by a reviewer, we explored the broader structure of correlations between behavioral and neural measures of craving. 30 We tested whether the correlations between SN/VTA and self-reporting craving (on the cueinduced craving task, CIC) were restricted to the deprived day. We found that they were not.
Self-reported craving for food was correlated with SN/VTA response to food cues on both of the non-fasted sessions (baseline session: r(38)=0.32; p=0.045; isolation session: r(38)=0.43, p = 0.006). Because the data for food craving measured during the cue-induced craving task is truncated at the upper limit of 10, we also analyzed the correlations using a truncated regression model (see Methods for details): For both non-fasted sessions, SN/VTA activity in response to 5 food cues remained associated with higher cravings ratings during the task (Baseline: b=0.02, t=2.11, p=0.034; Isolation: b=0.02, t=3.00, p=0.003). Self-reported craving for social interaction was correlated with SN/VTA response to social cues on both of the non-isolated sessions (baseline session: r(38)=0.37; p=0.018; fasting session: r(38)=0.35, p = 0.027). Thus, individual differences in the magnitude of SN/VTA response to a cue are correlated with simultaneously 10 measured self-reported craving for that cue, whether or not that cue has been selectively deprived.
Although our primary hypotheses focused on the dopaminergic midbrain, and particularly 15 SN/VTA, we also investigated responses in the striatum, a major target of projections from midbrain DA neurons 30 . A mixed effects regression model tested effects of cue (food, social, flowers) and session (fasting, isolation) for each subregion of the striatum separately (nucleus accumbens (NAcc), caudate nucleus (Ca), and putamen (Pu). We report results as significant at p<0.017 (0.05/3). 20 Food cues (compared to flowers) evoked a stronger response after fasting (compared to after isolation) in nucleus accumbens (NAcc, b=0.08, t=4.7, 95%CI [.05,.11], p<0.001; Figure 5 p=0.17). Similar to the SN/VTA, when compared to the baseline day, deprivation led to a decreased response to the non-deprived cue in striatal subregions, rather than an enhanced response to the deprived cue (an enhanced response to social cues following isolation compared to baseline in the caudate did not survive correction for multiple comparisons). For full results, see tables S5 -S12 in the SM. Thus, while the responses in SN/VTA were similar and overlapping for food craving and social craving, responses in the striatum were dissociable. We conducted exploratory analyses to test whether subjective craving of food and social contact (Craving_CIC) was correlated with striatal activity for any condition or any of the three subregions (reporting results significant at α<0.017
to correct for multiple comparisons) and found no significant correlation in the striatum ROIs (all 15 p-values>0.038).
We also conducted exploratory analyses testing the effects of food and social craving in other brain regions associated with craving. We selected four ROIs based on meta-analyses of craving . When compared to the baseline day, we find no significant effects in the exploratory ROIs. For full results, see tables S13 -S20 in the SM. 10 An exploratory whole brain random effects analysis yielded converging results: selective responses to food craving in nucleus accumbens, anterior cingulate cortex, periaqueductal gray, and amygdala, and selective responses to social craving in caudate nucleus, orbitofrontal cortex, and dorsomedial prefrontal cortex (see Tables S21 and S22 and Figure S4 in the SM). A 15 conjunction analysis assessing overlapping activation between the food contrast (fasting > isolation) and the social contrast (isolation > fasting) did not show any suprathreshold voxels.
We also calculated exploratory whole brain contrasts against baseline (i.e., food: fasting > baseline and social: isolation > baseline) but find no suprathreshold activity in this analysis. This result is in line with our ROI analysis in the SN/VTA showing similar magnitude of activity in in 20 response to food cues following fasting and baseline and in response to social cues following isolation and baseline.
Correlations between craving ratings.We explored whether individual differences in self- 25 reported cravings for different cues are correlated with each other. We computed the Pearson's correlation between each pair of average craving ratings (i.e., for food and social cues, measured after fasting, isolation and baseline). These correlations revealed some interesting structure ( Figure S5 ). First, 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, 30 z=1.64, p=0.05). Second, individual differences in cravings for social interaction were somewhat, but not significantly, more stable across the three sessions (mean r =0.33) than craving for food (mean r=0.06, z=1.26, p=0.10). Finally, the strongest correlation of all was between self-reported craving for food after fasting and for social interaction after isolation (r=0.64). These results suggest that there is a reliable individual difference in craving responses following deprivation, shared across both fasting and isolation, consistent with our observation 5 of a common neural response to both modes of deprivation.
To explore the differences in the results for the two craving measures (Craving_CIC and Craving_Q) in more detail, we included the following exploratory analyses: 1) We assessed the correlation between these measures and found that for both, food 10 and social craving the CIC and Q measures were correlated within session, across individuals (food r(38)=0.52; p<0.001; social: r(38)=0.30; p<0.032). 2) We tested whether the correlations between craving measures and SN/VTA activity was different for these two measures. We implemented two regression models, one for the fasting session and one for the isolation session.
The interaction between SN/VTA activity and craving measure (i.e., Craving_CIC and 15 Craving_Q) and its main effects were entered as predictors. We did not find a significant interaction of craving measure * SN/VTA activity for social craving ratings (b=37. 65 Craving ratings baseline. 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). Self-reported food craving on the isolation day was lower than on the baseline day (t(39)=3.59, p=0.001, two-25 tailed); but self-reported social craving on the fasting day was not lower than on the baseline day (t(39)=0.64, p=0.526, two-tailed). Figure S2 shows the craving ratings for each session including the baseline day.
of chronic loneliness showed a muted response in SN/VTA to both social cues (r(38)=-0.46, p=0.003) and food cues (r(38)=-0.37, p=0.018; two-tailed) during the baseline scan.
Similar to responses after isolation (see above), on the baseline day participants higher in chronic loneliness reported less social craving immediately before the scan (Social_Craving_Q, r(38)=-5 0.40, p=0.011; two-tailed), though chronic loneliness did not affect craving ratings during the task (Social_Craving_CIC, r(38)=-0.15, p=0.367; two-tailed).
In humans, acute mandatory social isolation evokes a neural "craving" response to social cues. 10 Midbrain regions showed selective responses to food cues after fasting and to social cues after isolation. SN/VTA activity was higher in people who self-reported wanting food or social interaction more, following deprivation. The multivariate pattern of SN/VTA response was similar for food and social interaction when craved. People who are forced to be isolated crave social interactions similarly to the way a hungry person craves food. 15 Our findings are consistent with results from the mouse showing that dopaminergic neurons in the midbrain represent the neural substrate of social isolation 16 . In mice, dopaminergic neurons in the midbrain appear to encode an aversive "loneliness-like" state that motivates social engagement. Our findings suggest that there is a similar mechanism underlying social craving in 20 humans.
Despite the fact that isolation lasted only ten hours, and the participants knew exactly when it would end, participants reported more loneliness and social craving at the end of the day than they did at the beginning. For people who are highly socially-connected, a day of social isolation 25 is a large deviation from typical rates of social interaction. Although when chosen intentionally, solitude can be restful and rejuvenating 31,32 , the externally mandated isolation was subjectively aversive.
Our primary, pre-registered hypotheses concerned activity in SN/VTA. SN/VTA includes almost 30 exclusively dopaminergic neurons, which respond with phasic firing to motivationally relevant cues (see 28 for review) and is activated by craving for food and for drugs of addiction [25] [26] [27] . Using a cue-induced craving task, we found that SN/VTA responded more to food cues after fasting and more to social cues after isolation. That is, although participants reported general discomfort and reduced happiness after both fasting and isolation, SN/VTA responses were selective to the deprived cue. The magnitude of SN/VTA response varied across participants, and was correlated 5 with self-reported craving for the corresponding cue: SN/VTA responses to food cues were correlated with self-reported craving for food (in all sessions), and SN/VTA responses to social cues were correlated with self-reported craving for social contact (also in all sessions). These results fit with the intuitive prediction that the deprivation of a need causes increased craving for the specific need 33 . The specific cravings evoke a generalizable pattern of activity in SN/VTA: 10 patterns of response in SN/VTA in response to food when hungry were more similar to the pattern of response to social cues when isolated than to responses to food when sated. Thus, a common signal at the core of the "craving circuit" in the SN/VTA responds selectively to motivationally salient deprived cues, independent of their specific content. 15 By contrast, food and social craving led to dissociable responses almost everywhere else in the brain. Both whole brain and exploratory ROI analyses revealed that food craving evokes selective responses in anterior cingulate cortex and (less reliably) in insula and amygdala but not in orbitofrontal cortex, while social craving evokes selective responses in orbitofrontal cortex but not anterior cingulate cortex, insula or amygdala. Even in the striatum, the major target of 20 dopaminergic projections from SN/VTA, craving for food and social contact were spatially dissociated. Fasting enhanced responses to food cues mostly in nucleus accumbens, whereas isolation enhanced response to social cues mostly in the caudate nucleus. Caudate nucleus activity during social craving is consistent with prior evidence of caudate activity when people re-live experiences of rejection by a significant other 34, for meta-analysis . Though social rejection 25 (being deliberately and specifically excluded from social interaction) is conceptually distinct from social isolation (being unable to access social interaction), both rejection and isolation could lead to increased social motivation 8 .
Food craving and social craving thus evoke both shared (SN/VTA) and unshared (striatum, cortical regions) neural responses. One open question concerns how these two kinds of craving might interact. That is, how does inducing food craving affect social motivation, or vice versa?
On one hand, there is some evidence that deprivation of one need leads to narrowed focus on the 5 deprived need to the exclusion of other needs. An unexpected finding of our study was that, compared to the baseline (no deprivation) session, neural activity for the non-deprived need was decreased, rather than activity for the deprived need being selectively increased. This pattern of narrowing activity was observed for both food and social cues. For example, the midbrain response to food cues was highest after fasting, slightly but not significantly lower at baseline, 10 and lowest after social isolation and eating to satiety. Putamen and nucleus accumbens responses to food cues were the same: not significantly higher after fasting than baseline, but significantly lower after isolation. These results fit with previous fMRI studies that find increased activity in midbrain and the "craving circuit" (i.e., striatum, OFC, ACC, amygdala and insula) in response to food cues after fasting compared to carefully induced satiety (e.g., 25, 35, 36 ), but not reliably 15 when compared to baseline (e.g., [37] [38] [39] ). Here we observed a similar pattern for social craving: midbrain responses to social cues were highest after isolation, slightly but not significantly lower at baseline, and lowest after fasting. 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. Depriving one 20 need might thus reduce motivation to pursue other needs. Indeed, there is some evidence that people are less prosocial when hungry 40 , consistent with a reduction in social motivation caused by acute hunger, although see 41 .
On the other hand, there is also some evidence that deprivation of one need leads to increased 25 motivation to pursue other sources of reward. In animal models social isolation can cause increased food consumption 42, for review , increased susceptibility to substance addictions, and other general changes in motivational systems 8, for review . That is, deprivation of social needs can result in generalized reward-seeking behavior, potentially as a form of compensation.
These two possibilities are not mutually exclusive: deprivation could lead to narrowed focus (reduced pursuit of other needs) or compensation (increased pursuit of other needs), depending on the duration and developmental timing of the deprivation. For example, short term acute isolation in adults may cause a temporary narrow focus on social connection, whereas long-term or developmental isolation could result in a shut-down of these adaptive efforts resulting in 5 social withdrawal and other compensatory changes in non-social motivation 43 . In our study, people who reported higher levels of pre-existing chronic loneliness showed reduced activity in the SN/VTA in response to social cues, consistent with the idea that chronic isolation can lead to social withdrawal ( 18,44 but see 19, 20 ) . However, the causal mechanism underlying the correlation with chronic loneliness remains unclear: it might as well be that individuals with generally 10 reduced sensitivity of motivational brain areas are more prone to becoming lonely. Indeed, people reporting high chronic loneliness in our study also had lower SN/VTA responses to food cues. Chronic loneliness could thus be a consequence, rather than a cause, of general low responsiveness in SN/VTA. 15 In addition, the effects of chronic loneliness on social approach motivation might also be mediated via other factors affecting approach behavior more broadly, such as anxiety or depression.
This ambiguity of correlational observations highlights the importance of our design, experimentally inducing acute isolation to disentangle direct effects of isolation per se from 20 individual differences in reward seeking behavior more generally.
There are two key limitations in this study that could be addressed in future research. 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 25 or money to fulfill a need. Responses in the passive cue-induced craving task could also be influenced by low-level processes like increased visual attention to the deprived category (though the robust prior literature in both humans and animals makes visual attention an unlikely explanation of SN/VTA engagement 28, for review ). We used two different measures of self-reported craving, and while these were correlated with each other, different craving measures were 30 significantly correlated with SN/VTA activity for food craving and to social craving. Thus, future studies should investigate the changes in subsequent behaviours that are predicted by SN/VTA responses following acute social isolation. Note though that also in domains like drug addiction and hunger, researchers have struggled to establish a gold-standard behavioral metric of subjective craving with better external validity than self-report 45 . 5 A second limitation is that we studied a small sample (n=40) of healthy well-connected young adults, mostly students, in one cultural context. Chronic loneliness disproportionately affects people older (i.e., the elderly 46 ) and younger (i.e., adolescents 14 ) than the participants studied here; future studies should test whether the current results generalize to those more vulnerable
populations. Yet despite our restricted sample, we observed substantial variability between 10 individuals at baseline, in both self-reported social craving and in SN/VTA response to social cues. We did not measure people's social behaviours at baseline (e.g., number and quality of social interactions) or other traits broadly relevant to social interaction (e.g., trait extroversion, depression, anxiety). Future studies should investigate how social craving and SN/VTA responses to social cues respond to changes in people's social environment over both short 15 (hours) and medium (weeks or months) time scales, within individuals 47 .
In all, our finding of 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, 20 similar to hunger. Thus, our research provides empirical support in human participants for the "social homeostasis" hypothesis developed based on animal models 43 52 . We performed power calculations for medium effects because social craving might be less intense or more variable than cue reactivity in drug craving and food craving. We therefore recruited 42 participants to account for potential attrition or 20 exclusion for MRI data quality; two participants were unable to complete all experimental sessions and so were dropped from analysis, leaving 40 complete datasets.
Participants were recruited via e-mail lists and through online advertisements and flyers.
Interested individuals filled out a screening questionnaire to assess eligibility for the study 25 (questionnaire data were collected using REDCap Software, Version 5.5.17). People were eligible if they reported a healthy Body Mass Index (BMI: [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] , no current calorie restricting diet, no permanently implanted metal in their body, no history of brain damage, and no currently diagnosed mental health disorder or substance abuse. Because we aimed to study social motivation in a sample of adults who have frequent and regular social interactions, we also excluded people who i) lived alone, ii) reported current feelings of loneliness on the UCLA Loneliness Scale 21 (i.e., we excluded people with scores above 50, which is one standard deviation above the mean for a student sample 21 ); or iii) reported smaller social network sizes than typically expected of adults 55 according to a social network size measure 56 and the Social Support Questionnaire 57 (i.e., we excluded people with social networks 2 or more SD below 5 mean, based on prior measured distributions from Von der Heide et al. 2014 58 ). All experimental procedures were approved by MIT's institutional review board, COUHES (couhes.mit.edu).
Participants signed a consent form describing all experimental procedures before participating in the study. Each participant was compensated with $350 for participating in three fMRI sessions and completing the 10 hours of food fasting and 10 hours of social isolation. 10 
Each participant was scanned in three fMRI sessions, separated by at least 24 hours. Figure 1 shows an overview of the experimental procedures. One session followed 10 hours of food fasting; one session followed 10 hours of social isolation, and one session was a baseline without 15 any mandated prior abstinence. All participants underwent all the three experimental conditions 
Participants were asked to abstain from consuming any food or drinks/coffee (except water) for 10 hours before the fMRI session. We scheduled each fMRI session at 7pm in the evening; thus, participants were asked to refrain from eating after 9am on the day of the 25 fasting session. We followed methods of previous food craving studies (for review: 26 ) in which participants are instructed to fast at home. Fasting was confirmed through self-report upon arrival. We also asked participants to abstain from all forms of exercising on the day of food fasting in order to avoid exhaustion. Participants filled out an online questionnaire, rating their momentary food craving, hunger, discomfort, happiness and dislike of fasting (on a visual analog 30 scale (VAS) anchored at 0 (not at all) to 100 (extremely)), every two hours during the food fasting period. Before the scan, participants were asked to select a meal that they would receive following the scan on an online food ordering platform (Grubhub 20 into problems which required assistance from the research team during isolation). Participants filled out an online questionnaire rating their momentary social craving, loneliness, discomfort, happiness and how much they disliked isolation (VAS anchored at 0 (not at all) to 100 (extremely)) every two hours during the social isolation period. The fMRI session was conducted immediately after the social isolation. Following the scan, a member of the research team chatted 25 with the participants about their experiences during isolation and made sure participants were not feeling troubled. Because living in a shared household was a prerequisite for participating in the experiment, participants were expecting to meet with at least one other person after the experiment.
Baseline. Participants came into the lab at 7pm and completed the same fMRI tasks as in the other two conditions (in addition to a functional localizer task, see below). Participants were asked to not be hungry at the time of the scan.
fMRI 5 Participants were in the scanner for around 1 hour in each session. We started with anatomical scanning. For each participant, structural whole-head T2*-weighted structural images were collected in 176 interleaved sagittal slices with 1 mm isotropic voxels (FOV: 256 mm). In addition, whole-head T1-weighted structural images in 176 interleaved sagittal slices with 1 mm isotropic voxels (FOV: 256 mm) were collected. The T2* weighted anatomical scan was 10 collected for anatomical identification of midbrain nuclei (i.e., the high content of iron in SN/VTA and red nucleus makes the T2* shorter and darker in these areas 28, 59 ). We confirmed the identification of mid-brain structures by registering to the newly available atlas of subcortical nuclei from Pauli et al. 60 and defined separate regions of interest in the dorsal and ventral striatum. We also collected a field map (phase-difference B0 estimation; TE1=3.47ms, 15 TE2=5.93ms) to control for spatial distortions, which are particularly problematic in midbrain fMRI 28, 59 . During acquisition of the anatomical images and the field map (~15min in total) participants lay quietly in the dark.
Subsequently, we collected functional data during six runs of a cue-induced craving task (see 20 below for details). Each functional run consisted of 147 volumes with 58 T2*-weighted echo planar slices (EPIs; TR=2000 ms, TE=30 ms, FoV=210 mm, 70x70 matrix, yielding a voxel size of 3x3x3 mm) acquired as a partial-head volume in an A-P phase encoding direction using interleaved slices. The scanning parameters were extensively piloted (N=11) using the functional localizer task (see below) and the parameters showing the best signal-to-noise ratio (SNR) were 25 selected for the study. Despite the small structure of the SN/VTA, we thus chose 3mm isotropic voxels because of their higher SNR compared to smaller voxels 61 This reduced geometric distortion to the point that standard distortion correction methods could 30 be applied 28 . The cue induced craving paradigm took approximately 26 minutes total.
We designed a novel CIC task to simultaneously measure craving for food and for social interaction, relative to a control. Participants viewed colored images depicting: i) groups of individuals as they meet, talk, laugh, smile, etc.; ii) different kinds of highly palatable foods such as cake, pizza, chocolate, etc., iii) attractive flowers as the 5 control condition.
On each trial, participants saw a single photograph and 3-5 word verbal description, for 5
sec. The combination of visual and verbal cues was intended to maximize deep semantic processing of the relevant attributes. Each trial was followed by a 1s rest period (during which a fixation cross was displayed). Three consecutive trials were presented in a block of the same 10 condition (food, social, control). Each block was followed by a jittered 2-6 second rest period.
Subsequently, participants self-reported how much they were currently craving food (on food blocks) or social interaction (on social blocks). After control blocks, participants rated how much they liked the flower image, in order to match the demand for response preparation. The stimuli for the CIC task were tailored to each individual's preferred foods and modes 20 of social interaction. During the initial screening, participants were asked to list their top ten favorite foods and social activities. Stock photographs illustrating these specific foods and activities were selected from a large public database (https://www.pexels.com/), and then verbal labels were added using the participant's own descriptions. Food descriptions included "fluffy syrup-drenched pancakes", "creamy cheesy macaroni", "refreshing mixed fruit salad", and 25 "yummy vanilla cake with sprinkles". Social descriptions included "chatting and laughing together", "joking around with friends", "supporting each other through workouts", "enjoying a conversation together." Social pictures were all matched for gender of participants (i.e., for a male participant, all social photographs included at least one man). The stimuli were images of strangers, rather than images of the participant's own friends and family, in order to (i) match the 30 food and control images for novelty, since SN/VTA activity is sensitive to novelty 62 , (ii) match image quality across conditions and participants, and (iii) avoid unmeasured variability in the quality or current status of participants' relationships with specific individuals. Control trials presented attractive photographs of flowers accompanied by positive valence verbal descriptions ( Figure 1) . For group-level results in response to cues (across sessions), see Table S23 and Figure S6 in the SM. A potential limitation of this approach is that it was targeting voxels responding to secondary reinforcers (i.e., money) instead of primary reinforcers (such as food and social contact). In animal models, dopamine neurons in the midbrain exhibit burst firing in response to both, primary reinforcers and also to conditioned stimuli once the conditioning is established (e.g., 65 and for review: 66 ). Conditioned stimuli are somewhat less effective than primary rewards 20 in terms of response magnitude and fractions of neurons activated 67 . Thus, by localizing 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. Importantly, both food and social cues would be similarly affected, so our key claim of similarity between these responses would not be undermined. In addition, our functional localization method was complementary to an 25 anatomical ROI, which would not be affected by this issue.
Before beginning the localizer task, participants memorized a set of 5 images depicting abstract art (all images taken from the free stock pictures site (https://www.pexels.com/)). During the task, the abstract art images served as cues to the condition of the current trial. The task had two conditions: a reward/loss condition (reward) in which participants could earn or lose money 30 depending on whether their responses were correct and fast enough, and a non-reward condition (non-reward) in which participants always received $0 regardless of their response. Each trial started with an abstract art image. The previously memorized (familiar) images indicated a nonreward trial. Abstract art images that were not previously observed (novel) indicated a reward trial. After the cue, participants saw a number between 1-9 (excluding 5) for 100ms on the screen. Their task was to press an assigned button indicating whether the number is below or 5 above 5 as fast as possible. Initially, correct responses were required in less than 500ms; after 10 consecutive correct answers, this window was reduced to 400ms. After they pressed the button, participants saw the outcome indicating whether they won $1 (reward trial, correct response, fast enough), lost $0.20 (reward trial, wrong response or too slow), or received $0 (non-reward trial).
In total, participants played 80 trials (40 trials per condition) and the duration of the task was 10 approx. 10 minutes. Participants responded correctly and within the time limit on 87% of reward trials and 69% of non-reward trials. The earnings from this task were added to participants' compensation after the baseline session. This design allowed us to compare responses to novel stimuli predicting reward versus familiar stimuli predicting no possibility of reward. For grouplevel results within the midbrain in response to reward > non-reward, see Figure S7 in the SM. relative to the sample's average social network size. In addition, we calculated a loneliness score for each participant using the UCLA loneliness scale 21 . We tested how self-report ratings of hunger, food craving, discomfort, happiness and dislike of fasting provided during food fasting 25 changed over the course of 10 hours using paired t-tests comparing the first rating (collected at beginning of fasting) and last rating (collected after 10 hours of fasting). We used the same analysis for the ratings provided during social isolation: loneliness, social craving, discomfort, happiness and dislike of isolation. Data distribution was assumed to be normal but this was not formally tested. Three participants missed filling out the 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.
fMRI Data analysis
We used open source preprocessing pipelines for fMRI data, developed through 5 the nipy and nipype 68 initiatives. We used the heudiconv python application which uses dcm2niix to convert raw scanner data into the NIFTI image file format, then organizes that data into a BIDS-formatted directory structure. The FMRIPrep application 69 was used to minimally preprocess the anatomical and functional data (using default settings but including susceptibility distortion correction using fieldmaps (see below)). Using FMRIPrep, we skull-stripped 10 anatomical images first roughly using the atlas-based ANTS program 70 , and then refined it using information from Freesurfer surfaces after reconstruction was completed 71 . Brain tissue segmentation was performed with the FMRIB Software Library (FSL) FAST program 72 . Images were spatially normalized to 2mm isotropic MNI-space using the multiscale, mutual-information based, nonlinear registration scheme implemented in ANTS. We visually inspected brain masks, 15 tissue segmentation and freesurfer surfaces. Susceptibility distortion correction was performed using phase-difference B0 estimation 73 .
A reference image for each run was generated from the input BOLD timeseries. A functional brain mask was created using a combination of FSL, ANTS, AFNI and nilearn tools 74 .
Using FSL's MCFLIRT program 75 , we estimated and corrected for head motion, resulting in a 20 coregistered BOLD series as well as motion-based confound regressors. Any run containing a framewise displacement greater than 0.4 mm on more than 25% of the total frames was excluded from additional analyses. Additional confound regressors were generated, including other measures of motion (framewise displacement and DVARS and anatomical CompCor 76 timeseries derived from CSF and white matter tissue segments). The reference image of each run was 25 aligned with the anatomical image using FreeSurfer's program "bbregister" 77 . The timepoint-tofunctional reference transformation, the functional reference to anatomical transformation, and the anatomical-to-MNI transformation was concatenated into a single transformation and used to transform each functional timeseries into MNI template space. Spatial smoothing was performed on the FMRIPrep outputs with a 6mm smoothing kernel using FSL's SUSAN tool 78 , which uses 30 segmentation boundaries to avoid smoothing across tissue types. MRIQC, an opensource quality assurance software tool 79 , was used to generate additional reports which display Image Quality Metrics (IQMs).
Modeling. Analyses were conducted using the nipype framework 68 . For run-level analyses, the preprocessed timeseries was assessed with algorithms from the Artifact Removal Toolbox 5 (ART) 80 to identify frames within the run that have an abnormal amount of motion (0.4 mm of total displacement, or an intensity spike greater than 3 standard deviations from mean). The design matrix included boxcars for the experimental conditions convolved with a double-gamma hemodynamic response function (HRF), and nuisance regressors representing frame-wise motion, the anatomical CompCor regressors derived from white matter and CSF, as well as 10 impulse regressors for volumes identified by ART. A high-pass filter (120 Hz) was applied to the design matrix and the smoothed data. The model was evaluated using FSL's FILM program 81 .
Subject-level contrast maps were generated using FSL's FLAME 81 in mixed-effects mode.
Exclusion criteria were pre-established in our pre-registration 15 (https://osf.io/cwg9e/). We excluded 3 runs of fMRI data from the overall sample (from 2 participants from the Cue Induced Craving task) based on the following criterion: Any run containing a framewise displacement greater than 0.4 mm on more than 25% of the total frames will be excluded from additional analyses. 20 ROI definition. We included functional voxels which overlapped at least 75% with the substantia nigra pars compacta (SN) and the ventral tegmental area (VTA) region (number of voxels=161) from the probabilistic atlas of human subcortical nuclei 60 . Because the striatum is a major target of projections from midbrain neurons and their firing results in increased DA transmission in the striatum 30, 82, 83 , we expected to see the same pattern of results in the striatum, 25 i.e.: increased activation to food cues after food deprivation and to social cues after social isolation and a positive correlation between activity in striatum and self-reported craving (for both, food and social craving). Thus, we also included 3 additional ROIs in our analysis:
putamen (Pu; number of voxels = 2530), Caudate Nucleus (Ca; number of voxels = 2523) and
Nucleus Accumbens (NAcc; number of voxels = 300) also using the probabilistic subcortical 30 atlas 60 .
To define subject-specific ROIs, we used individual activations of each participant in the localizer task. The fMRI time series were analyzed using an event-related design approach implemented in the context of the GLM. The model contained two regressors separately modeling the presentation of novel/reward cues, and familiar/non-reward cues (i.e., 5 when the abstract art images were presented, 2s). We also included one regressor for the time period of button press and outcome (1.1s). Because we did not add any jitter between button press and presentation of outcome (as this was not the contrast of interest), we modeled the whole segment as one block. For each participant, we calculated the target contrast novel reward > familiar non-reward. We then used a mask encompassing the whole midbrain as the search 10 space for the selection of individual voxels. In each participant we selected the top 100 active voxels within the search space in response to the target contrast. . We selected all voxels that showed a minimum probability of 50% for being in the respective area and extracted the mean activity from those voxels in 30 response to food, social and control cues for each session (fasting, baseline and isolation). We then calculated a mixed model regression for each region within this "craving circuit" (and report results as significant at p < 0.0125 (0.05/4)). In addition, due to previous findings showing that ventral striatum and nucleus accumbens are activated during craving for food and drugs 85 and associated with altered responses in chronic loneliness 18, 20 , we explored this region further at the suggestion of a reviewer by selecting the top 100 voxels within the NAcc that were active in 5 response to the midbrain localizer task (i.e., active in response to reward anticipation) and assessed whether these voxels code for food and social craving (see SM for results). used maximal models, including participants as a random effect with both random intercepts and random slopes. Data distribution was assumed to be normal but this was not formally tested. We 15 modeled these effects in the anatomically defined SN/VTA (pre-registered analysis) and in the functionally defined ROI of voxels maximally sensitive to reward and novelty (exploratory analysis). Thus, for each ROI, we tested two mixed effects models:
First, we tested our key hypothesis, that responses to food cues (relative to flowers (control)) 20 would be higher after fasting, and responses to social cues (relative to control) would be higher after isolation. In this model, the fixed effects were: session (fasting vs isolation; contrast coded), cue (control, +food, +social; indicator coded), the interactions of cue and session, and mean framewise displacement in that session; we also included random intercepts and random slopes for each participant. 25 
To test whether responses were different on the deprivation days, compared to the baseline day,
we modeled responses on all three sessions by including fixed effects of session (baseline, +fasting, +isolation; indicator coded), cue (control, +food, +social; indicator coded), the interactions of cue and session, and mean framewise displacement in that session; we also 30 included random intercepts and random slopes for each participant. 32 The command for both models was:
To test whether these responses were correlated with individual differences in selfreported craving, we calculated the average contrast value (food>flowers and social>flowers) in the anatomically defined SN/VTA for each participant. We used two different approaches to 5 measure participants' self-reported craving. First, we calculated the mean craving rating participants reported on each trial during the CIC task in the scanner (Craving_CIC). This measure was exactly comparable across sessions, and simultaneous with the fMRI data acquisition. Because the data for food craving were truncated at the upper limit of 10 (see results it can be compared to the self-report at the beginning of each session. In addition, the Craving_Q ratings were completed on a finer scale (0-100 instead of 0-10) and participants had no time restriction when filling out the questionnaires (while during the task, participants had 5s to complete the scales). For these reasons, we include both types of craving ratings and report results as significant at α <0.025. We measured correlations between self-reported craving and 20 neural responses for each deprivation session. Because we specifically predicted a positive correlation between craving for the deprived target and response magnitude in SN/VTA, these correlations were tested one-tailed. Data for the analyses was extracted using FSL's 'fslmeants' utility and subsequent univariate and correlation analyses were conducted in Matlab 2019b, and to the dissimilarity between food_noncraved x food_craved (i.e., food-isolation and foodfasted; same stimuli but different motivational state (we used isolation instead of baseline as it represents the satiated control condition to fasting)); both comparisons used pair-wise t-tests.
Data distribution was assumed to be normal but this was not formally tested. 25 Correlations univariate fMRI measures and behavioral data. In follow-up analyses, we tested whether individual differences in social network size predict the magnitude of self-reported and neural measures of social craving. We used the UCLA loneliness score obtained from each participant as an indicator of their chronic loneliness. We correlated these two measures with mean values extracted from the midbrain and striatum ROIs from the contrast social_craved>control.
5 SN/VTA: Multivariate decoding of food cues. As a proof of concept, we tested if we could discriminate fMRI patterns between food and control cues in the fasting session in the SN/VTA.
For each participant, we partitioned the data into six independent folds (6 runs), and iteratively trained a linear support vector machine (SVM) classifier on 5 runs (i.e. 15 beta estimates per condition) and tested on the left-out run (3 beta estimates per condition). We then averaged the 10 classification accuracy across runs to yield a single estimate for each participant. This withinsession classification tested whether we would be able to decode the cue type (food vs. control) from multivariate patterns within the SN/VTA. We tested whether the accuracy was significantly above chance using bootstrapping. We first generated a null-distribution of the data by shifting the mean to be 0.5 (by calculating dataset -mean(dataset) + 0.5) and then generated 1000 15 datasets randomly by sampling with replacement from the classification accuracies across subjects using Matlab's bootstrp function. We then calculated a mean for each of the 1000 simulated datasets from the null distribution and compared it to our observed data average. We calculated the probability p of obtaining a mean value across the 1000 datasets that is equal or higher than the actual mean from the original dataset and rejected the null hypothesis of no 20 group-level decoding if p < 0.05. For obtaining confidence intervals of the mean in the data samples, we generated 1000 datasets randomly by sampling with replacement from the classification accuracies across subjects using Matlab's bootci function. We found above chance (50%) decoding of food cues from flower images after fasting (mean accuracy=0.5556; p<0.001, 95% CI=0.527-0.585). 25 
We ran the same analysis as reported in the main manuscript on data for which we did not perform any smoothing during preprocessing in order to test whether smoothing has substantial effects on our results.
First, we tested how smoothing affected the accuracy to discriminate fMRI patterns between 30 food and control cues in the fasting session in the SN/VTA using the same method as described above ("SN/VTA: Multivariate decoding of food cues) on the unsmoothed data. Here, we find that omitting 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). Then, as in the original analysis, we trained a classifier on the pattern of activity in the SN/VTA after fasting to food cues and flower 5 images and tested whether it would successfully decode food and social cues from control cues in the other two sessions (baseline and isolation). Again, we generated null distributions using the permutation analysis described in the main text (following Stelzer et al. 2013 94 Exploratory whole brain group analyses. As a complementary analysis, we assessed main effects and interactions of cue and session on whole brain activity. We set up a flexible factorial model with the factors subject, session (baseline, fasting, isolation) and cue (food, social). We entered the first-level contrasts food > control and social > control for each session into the group-level analysis. Analyses were implemented in SPM12b. 20 Main effects: Cue. We first assessed whole mean brain activity for the contrasts: food > control and social > control across all experimental sessions (baseline, fasting, isolation) as a manipulation check to assess which regions were activated in response to food (vs. control) and social (vs. control) cues. We corrected for multiple comparisons using whole brain family-wise error correction. Table S21 and Figure S7 show the results from this analysis. 25 Interaction effects: Cue * deprivation. We then assessed effects of isolation on brain activity in response to social cues and effects of fasting on brain activity in response to food cues in the whole brain. To do this, we calculated two contrasts: 1) food: fasting > isolation and 2) social:
isolation > fasting. 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 30 Gaussian random fields approach at cluster-level with a voxel-level intensity threshold of p < 0.001 96 . We note that this method of correction for multiple comparisons is likely biased towards false positives 97, 98 . We decided to share these lenient analyses (Tables S22 / S23 and Figure S8) to be more complete and for potential future meta-analyses.
Midbrain localizer: group analysis. As described above, we used a functional localizer to select 5 the most active voxels for reward anticipation and novelty within the midbrain to functionally localize the SN/VTA in each participant individually. To explore the localization of midbrain responses to the localizer task in more detail, we implemented a group random effects analysis entering the first level contrast reward > non-reward from each participant (see Figure S9 ). 10 Data analysis was not performed blind to the experimental conditions. 