Part 3:Functional Connectivity Between Memory And Reward Centers Across Task And Rest Track Memory Sensitivity To Reward

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Omnibus ANOVA across rest and encoding task—To compare rest connectivity with background task connectivity, we used connectivity measures from low-passed filtered time series to match the task and rest preprocessing. Connectivity measures were submitted to the same ANOVA as reported above, with the task stage factor having three values (pre-encoding, encoding, post-encoding). This analysis allows us to directly test the hypothesis that patterns of connectivity and their relationship to behavior are stable across tasks and rest. The results of the ANOVA are reported in Table 2, and all connectivity values are depicted in Fig. 4b. From the effects of interest, we found a main effect of modulator status, with greater connectivity in modulators (M= 0.52, SD= 0.14) than non-modulators (M= 0.32, SD= 0.21). We also found a significant interaction between modulator status and reward structure. Follow-up two-sample t-tests showed that the interaction was driven by significant differences between modulators and non-modulators in hippocampus/PHC connectivity with ACC, MPFC, OFC, and VS (all t(22) > 2.1, p< 0.05) but not with midbrain (t(22) = 0.90, p = 0.377). The effect of modulator status and the interaction between modulator status and reward structure were not driven by a median split of participants and were replicated when memory sensitivity to reward was treated as a continuous measure using ANCOVA (main effect of BRM: F(1,22) = 7.13, p= 0.014, η2P= .25; BRM*reward structure interaction:

F(2.80,61.58) = 3.25, p= 0.031). We found no effect of task stage and no interaction between task stage and modulator status in this analysis (both p> 0.2), suggesting connectivity between memory and reward networks and its relationship to behavior remained relatively stable across the pre-encoding, encoding, and post-encoding scans.

Summary of the ANOVA findings—Both omnibus and rest-only ANOVAs showed an effect of modulator status on memory-reward region connectivity and revealed that the modulator effect was not driven by all connections equally. A set of reward regions disproportionately drove the overall effect: ACC, OFC, and VS connectivity with hippocampus/PHC consistently differed between modulators and nonmodulators, whereas midbrain connections consistently did not. While we found an overall increase in connectivity between reward and memory regions from pre-encoding to post-encoding in the rest-only ANOVA, there were no interactions between modulator status and task stage in either analysis. These results are consistent with the idea that there are stable individual differences in brain connectivity patterns that relate to behavior.

Functional relationships among connections

Rest-only PCA—To investigate which reward regions may be a part ofthe same functional network, we investigated the cross-correlation structure of connectivity values across participants. Pairwise correlations of all rest-based connectivity values are presented in Fig. 5a. After separating them into principal components, we found four components that explained at least 10% of the variance in the connectivity values and were retained for further analyses (Fig. 5b). Monte Carlo simulations (Fig. 6a) indicated that all components explained more variance than expected by chance (all p< 0.01), except for component 3, which was marginal (p= 0.083). Preferential loading of each reward region on different components was tested using one-way ANOVA (Fig. 6b).

The first component explained 28.2% of variance in connectivity scores and loaded preferentially on ACC, OFC, and VS connections and, to a lesser extent, MPFC and midbrain (one-way ANOVA, F(4,15) = 3.91, p= 0.023). The second component explained 16.6% of the variance and was loading preferentially on midbrain connections and, to a lesser extent, negatively on OFC and MPFC connections (one-way ANOVA, F(4,15) = 38.2, p< 0.001). The third component explained 12.5% ofvariance and loaded comparably across all reward regions (one-way ANOVA F(4,15) = 0.74, p= 0.580). The fourth component explained 11.6% of variance and was loading preferentially on MPFC connections (one-way ANOVA F(4,15) = 5.26, p= 0.008). Multiple regression then tested the relationship between the component scores and behavioral reward modulation across participants. The first component score significantly tracked behavioral reward modulation (beta = 7.09, SE= 3.13, p= 0.03) while the remaining components did not (all p> 0.18). These results indicate ACC, OFC, and VS connectivity with hippocampus/PHC co-vary and jointly track memory sensitivity to reward. Midbrain and MPFC connectivity covaried to a lesser degree with the other reward-related regions and with each other. Furthermore, the components that midbrain and MPFC most strongly loaded on did not track memory sensitivity to reward, opening the possibility that they may each be a part of functionally different systems.

The lack of differentiation between modulators and non-modulators based on hippocampus/ PHC-midbrain was unexpected, especially given that significant effects of connectivity were observed for other reward-related regions in both ANOVA and PCA. While reward modulatory effects on midbrain interactions with memory regions have been documented previously (Gruber et al., 2016; Wolosin et al., 2012), the effect may not result in increased sensitivity to reward per se. Instead, overall greater interactions—irrespective of external rewards—may lead to greater dopamine availability in the hippocampus and greater memory overall (Duncan et al., 2014; Lisman et al., 2011; Tompary et al., 2015).

To test this idea, we performed a second multiple regression with component scores as predictors but this time using overall accuracy as an outcome. The second component significantly predicted the overall accuracy across participants (beta = 0.28, SE= 0.11, p= 0.02), while the other three components did not (all p> 0.5). Thus, a distinct pattern of resting-state connectivity, captured by the second component loading preferentially on the hippocampus/PHC-midbrain connectivity, also was relevant to behavior but tracked overall memory rather than memory sensitivity to reward.

Omnibus PCA across task and rest—To test the degree to which cross-correlations may remain stable across task and rest, a second PCA was performed on the whole set of 30 connectivity values per subject (10 connections for each pre-encoding, encoding, and post- encoding periods). Connectivity values derived from low-pass filtered time series were used to match pre-processing across tasks and rest. Three components explained more than 10% of the variance. Monte Carlo simulations (Fig. 6a) indicated that only components 1 and 2 explained more variance than expected by chance (both p< 0.001). Component 3 did not (p = 0.8) and thus was not considered further. The loadings of each reward region on the two retained components are presented in Fig. 6c.

The first component explained 31.4% of the variance and showed marginal differences between loadings among reward regions (one-way ANOVA F(4,25) = 2.30, p= 0.086), with numerically greatest loadings of ACC and VS. The second component explained 18.0% of the variance and most strongly loaded on midbrain connections (F(4,25) = 4.78, p= 0.005). Multiple regression with component scores as predictors and BRM as an outcome showed that the first component was a significant predictor of Brm (beta = 2.99, SE= 1.00, p= 0.007), while the other was not (p> 0.09). A second multiple regression with the overall accuracy as an outcome did not reveal any significant effects (all p> 0.2). Thus, the omnibus PCA partially replicated the rest-only PCA. Taken together, the PCA findings indicate that: (1) connectivity patterns of ACC, VS, and potentially OFC are functionally coupled, with a composite connectivity score that most strongly loaded on these regions tracking memory sensitivity to reward, (2) midbrain and potentially MPFC connectivity patterns co-vary to a lesser degree with the other reward regions and each other, and (3) resting-state hippocampus/PHC-midbrain connectivity may be more predictive of overall memory performance than memory sensitivity to reward.

Predicting memory sensitivity to reward from connectivity patterns using machine learning The ANOVA and PCA findings reported above showed that connectivity between memory and reward-related regions tracked individuals’ memory sensitivity to reward and that this relationship may be relatively preserved irrespective of task stage. To more directly quantify how well patterns of connectivity may differentiate between modulators and nonmodulators, we applied SVC to predict modulator status from connectivity patterns in each task stage (inputs = 10 connectivity values, outputs = modulator status). Classification accuracy that occurred with a probability of less than 0.017 (0.05/3 to correct for multiple comparisons) was considered significant. Using cross-validation, we found that classification of modulator status from connectivity patterns was reliably above chance in the pre-encoding scan (accuracy = 79.2%, p= 0.010) and the encoding scan (accuracy = 75%, p= 0.006). Classification based on the post-encoding scan was lower and not reliably different from chance (accuracy = 58.3%, p= 0.157; Fig. 7a). Although the classification accuracy based on post-encoding connectivity did not reach significance, we did not find evidence that the probability of misclassification would be reliably greater at post-encoding compared to pre- encoding or encoding task stages (both χ2 < 2.1, p> 1.5).

To verify that successful classification did not depend on treating reward modulation as a dichotomized variable, ε-SVR was conducted to predict behavioral reward modulation as a continuous measure. Consistent with the SVC findings, cross-validated SVR revealed that connectivity between memory and reward regions reliably predicted BRM during the encoding scan (r= 0.51, p= 0.010), and marginally predicted during the pre-encoding rest scan (r= 0.46, p= 0.024). The SVR on post-encoding connectivity did not reach significance (r= 0.35, p= 0.098; Fig. 7b).

Complementary connectivity analyses

Correlations between connectivity changes and behavior—Though we did not find interactions between task stage and reward modulation of memory in the rest-only ANOVA, a changing relationship between connectivity and behavior may be better characterized by a correlation of task-induced increases in connectivity with behavior (Gruber et al., 2016; Murty et al., 2017; Tambini et al., 2010). To test the effect of connectivity increases on behavior, we ran exploratory correlations between pre-to-post connectivity increases and both BRM and overall accuracy (Table 3). None ofthe memory- reward ROI connections predicted BRM (all p> 0.15, uncorrected for multiple comparisons) nor overall cued recall rates (all uncorrected p> 0.05). There was a marginal correlation between PHC-midbrain connectivity and overall accuracy (r = 0.40, p= 0.052), providing a partial replication of prior reports on midbrain connectivity changes tracking behavior (Duncan et al., 2014; Gruber et al., 2016).

Anterior and posterior differences within the hippocampus—Functional differences between anterior and posterior hippocampus were tested in a 3 (task stage) × 2 (hippocampus ROI: anterior, posterior) × 5 (reward ROI) repeated measures ANOVA with modulator status as a between-subjects factor. The results for the ANOVA are reported in Table 4, and all connections are displayed in Fig. 8. We found the main effect of modulator status, indicating that hippocampal connectivity with reward regions tracked memory sensitivity to reward. We also found a significant interaction between reward ROI and hippocampal division, indicating that the anterior and posterior hippocampus is preferentially connected to distinct reward regions. Of main interest were interactions between hippocampal division and modulator status that would indicate that anterior and posterior portions of the hippocampus differentially predict reward modulation of memory. However, all interactions that included modulator status and hippocampal ROI as factors were non-significant (all F< 1.5, p> 0.3), providing no evidence for such dissociation.

As the last analysis, we tested whether connectivity increases from pre-to post-encoding rest may be differentially related to behavior for the anterior and posterior hippocampus. Thus, we correlated BRM with post-pre connectivity changes in each connection, separately for the anterior and posterior hippocampus. No significant correlations were found (all |r| < 0.32, all p> 0.05).

Discussion

The current study measured functional connectivity between memory and reward regions before, during, and after a monetary incentive encoding task. Using both standard and machine learning approaches, we observed that connectivity of hippocampus and PHC with reward-related regions tracked individual differences in memory sensitivity to reward. This effect was differentially driven by a subset of reward regions: ACC, OFC, and VS connectivity patterns co-varied together across participants and were consistently predictive of individual differences in reward modulation of memory. Midbrain connectivity with hippocampus and PHC was not related to memory sensitivity to reward, and the MPFC connectivity with memory regions varied across analyses. The relationship between connectivity and reward modulation of memory was present prior to encoding and during encoding, with weaker but not reliably different effects during post-encoding rest. Overall connectivity between memory and reward regions increased from pre-to post-encoding rest, but connectivity changes were not significantly related to behavior. These results demonstrate stable individual differences in intrinsic communication between memory and reward-related regions that track individual differences in reward modulation of memory.

Furthermore, the results implicate a wider set of reward-related regions in memory modulation by reward than previously considered.

Theoretical perspectives have emphasized the role of the midbrain, and to some degree VS, in memory modulation by reward (Lisman & Grace, 2005). A central finding ofthe current study is that connectivity of reward regions VS, ACC, and OFC with hippocampus and PHC was related to the degree to which individuals’ memory was impacted by reward. The role of VS in reward modulation of memory is predicted by current models, which postulate that VS integrates and relays dopamine signals from the hippocampus to the midbrain (Lisman & Grace, 2005). A prior study on reward-motivated encoding found univariate activation in VS covaried with hippocampus and PHC, further tracking the impact of reward on memory (Adcock et al., 2006). Our study, however, is the first to show connectivity between VS, jointly with OFC and ACC, and memory centers in hippocampus and PHC predicted individual differences in reward modulation of memory, irrespective of when connectivity was measured. Interestingly, background VS-hippocampal connectivity is also observed in procedural learning when procedural learning is rewarded (Hamann, Dayan, Hummel, & Cohen, 2014). Thus, while the traditional multiple memory systems view postulates a division of labor between the hippocampus, supporting declarative memory, and striatum, supporting procedural memory (Squire, 1992), there may be a strong degree of interactions between these systems supporting both episodic memory and procedural learning (Doll, Shohamy, & Daw, 2015; Hamann et al., 2014; Kafkas & Montaldi, 2015; Wimmer, Daw, & Shohamy, 2012).

Our study also implicated two reward regions previously not considered in the context of reward-motivated learning—the OFC and ACC—which showed similar profiles to VS. While novel in the area of reward modulation of memory, prior work has reported hippocampal connectivity with ventromedial/orbitofrontal regions across various contexts of learning (Gluth, Sommer, Rieskamp, & Büchel, 2015; Ranganath, Heller, Cohen, Brozinsky, & Rissman, 2005; Tsukiura & Cabeza, 2008; Zeithamova, Dominick, & Preston, 2012). For instance, background hippocampal-OFC connectivity inversely tracks the transfer of learned reward to related experiences (Gerraty et al., 2014). In a sensory preconditioning paradigm, participants encoded face-face pairs and then were trained to associate a gain, loss, or no value with one ofthe faces. Hippocampal-OFC connectivity was negatively correlated with the transfer, meaning that participants with stronger connectivity showed a lesser tendency to extend the learned face value to related faces. Thus, although the role of interactions between memory regions and OFC may vary depending on the specific task, our study converges with prior work to show that participants with greater hippocampal-OFC connectivity are more likely to differentiate events based on their explicitly assigned values.

The functional relevance of ACC in memory in humans is currently less understood. During rest, ACC demonstrates functional connectivity with both hippocampus and PHC (Cao et al., 2014; Margulies et al., 2007). In rodents, the ACC has been implicated in the consolidation of learned associations into long-term memory, showing coordinated cellular changes with the hippocampus (Wang, Tse, & Morris, 2012; Weible, Rowland, Monaghan, Wolfgang, &Kentros, 2012). Here, ACC, OFC, and VS connectivity with hippocampus and PHC covaried across participants. While intrinsic, coordinated activity between the hippocampus, ventral striatum, and OFC has been reported previously (Gerraty et al., 2014; Kahn & Shohamy, 2013), our data implicate that ACC may comprise a functional network with VS and OFC.

Although ACC and OFC may show distinct functions in some tasks (Luk & Wallis, 2013),the ACC, OFC, and VS all share similar functions during reward processing, such as tracking anticipated reward values and outcomes (Amiez et al., 2006; Bialleck et al., 2011; Knutson, Taylor, Kaufman, Peterson, & Glover, 2005; Yan et al., 2016) and signaling the value of choices (Boorman, Behrens, Woolrich, & Rushworth, 2009; Rogers et al., 2004; Strait, Blanchard, & Hayden, 2014). Together, these regions may serve to promote motivationally salient behaviors or suppress motivationally irrelevant behaviors (Hare, O’Doherty, Camerer, Schultz, & Rangel, 2008; Kaping, Vinck, Hutchison, Everling, & Womelsdorf, 2011; Nieuwenhuis & Takashima, 2011; O’Doherty, 2011; Walton, Chau, & Kennerley, 2015). Greater background hippocampal and PHC interactions with ACC, OFC, and VS may reflect greater availability of reward signals to memory structures, resulting in enhanced modulation of memory by reward in our paradigm. While the unique contribution of each region will require further inquiries into their individual roles in motivated learning, our data provide several new insights into their functional interactions.

An unexpected aspect of the current data was the U-shaped relationship between reward value and memory found in the fMRI sample. While this finding was unexpected and not replicated in the accompanying behavioral sample, it is neurobiologically plausible. U- shaped responses to reward have been identified in the medial frontal and medial orbitofrontal cortices (Elliott et al., 2003): notably, regions, such as the ACC and VS, signal the reward prediction error, or the discrepancy between expected and experienced rewards (Abler, Walter, Erk, Kammerer, & Spitzer, 2006; Fiorillo, Tobler, & Schultz, 2003; Montague, Dayan, & Sejnowski, 1996; Silvetti, Seurinck, & Verguts, 2011). These signals do not reflect the absolute value of losses and gains but rather the relative value, flexibly scaling around an expected reference point (Seymour & McClure, 2008; Tobler, Fiorillo, & Schultz, 2005). In the current task, the dime trials may have served as a reference condition, with dollar trials being perceived as rewards and penny trials as relative losses. The salience of both losses and gains can impact performance (Bartra et al., 2013; Shigemune et al., 2014), manifesting as the U-shape in cued recall accuracy across the reward conditions. Taken together, reward effects on memory may reflect the influence of reward prediction error signals on encoding, which may be non-linear in nature.

Contrary to prior studies, we did not find a relationship between hippocampal-midbrain connectivity and reward modulation of memory, even when the hippocampus was separated into anterior and posterior sections (Adcock et al., 2006; Gruber et al., 2016; Murty et al., 2017; Wolosin et al., 2012). Rather, we found a correlation between a component score from the PCA that loaded preferentially on midbrain-hippocampus/PHC connections and overall accuracy. While this finding was not expected apriori, it is consistent with the proposal that interactions between the midbrain and medial temporal regions are not unique to externally motivated rewards but support encoding and consolidation in general (Lisman et al., 2011). Consistent with this view, the midbrain has been shown to interact with the hippocampus in encoding tasks that do not involve reward (Duncan et al., 2014; Zeithamova, Manthuruthil, & Preston, 2016), with both background connectivity during encoding (Duncan et al., 2014) and rest connectivity after encoding (Tompary et al., 2015) tracking associative memory. Our findings suggest that midbrain connectivity during reward-motivated encoding may be more relevant to overall associative memory than reward modulation of memory and potentially plays a distinct functional role in memory from other reward-related regions.

Of note is the relatively short delay between encoding and test in our paradigm that also may affect the pattern of midbrain effects observed. Reward-related memory effects have been documented during immediate tests of memory (Gruber et al., 2016; Wolosin et al., 2012, 2013) but appear stronger after overnight consolidation (Patil, Murty, Dunsmoor, Phelps, & Davachi, 2017; Tompary et al., 2015; Wittmann et al., 2005). The delayed impact of reward on memory performance is thought to reflect enhancement of dopamine-dependent consolidation processes by rewards (Lisman et al., 2011). Thus, while midbrain interactions with hippocampus and PHC predicted overall memory in our paradigm, it is possible that an additional midbrain contribution to reward-enhanced consolidation would be observed if recall had been performed after a 24-hour interval had elapsed.

One reward region that did not show a consistent relationship to behavior in our paradigm was MPFC. Connectivity with MPFC was predictive of memory sensitivity to reward in the omnibus ANOVA across rest and task, but the relationship to behavior did not replicate in other analyses. Although prefrontal reward regions like OFC and MPFC are spatially proximal and often considered together, the PCA results indicate that they may participate in partially distinct networks. A more conclusive characterization ofthe role ofMPFC in motivated encoding and memory, in general, awaits future studies.

In addition to implicating a broader set of reward regions in motivated encoding than previously considered, a second key contribution of our study is novel evidence for the connectivity fingerprint hypothesis (Finn et al., 2015; Gratton et al., 2018). Measuring patterns of connectivity before, during, and after motivated encoding, we found that individual differences in reward modulation of memory were predicted by connectivity between memory and reward regions, irrespective ofwhen connectivity was measured. Though post-encoding connectivity was less consistently related to behavior, we found no reliable differences in the predictability of behavior between the task stages. These results are in line with the recent findings of stable connectivity patterns and their relationship to individual differences in cognition (Finn et al., 2015; Gratton et al., 2018; Poole et al., 2016; Touroutoglou, Andreano, Barrett, & Dickerson, 2015; Wang et al., 2010), newly extending them to the area of motivating encoding.

Our focus on stable, individual differences in connectivity complement other approaches to linking connectivity to behavior, including memory. Resting-state functional connectivity can change on a short time-scale in response to a task (Tambini et al., 2010; Urner, Schwarzkopf, Friston, & Rees, 2013), with learning-related connectivity changes relating to memory performance (Gruber et al., 2016; Murty et al., 2017; Tambini et al., 2010; Urner et al., 2013). During task performance, the across-region coupling may change even more rapidly, differentiating between memory task conditions at the order of minutes or even from individual trial to individual trial (Kafkas & Montaldi, 2015; Rissman, Gazzaley, & D’Esposito, 2004; Zeithamova et al., 2012; Zeithamova et al., 2016). For example, Kafkas and Montaldi (2015) showed that hippocampal-VS connectivity was greater during the encoding of unexpected versus expected stimuli. Thus, both the stable and variable aspects of connectivity provide information relevant to cognition.

Which aspects of connectivity (stable or task-induced) are more pronounced or most relevant to behavior is an open question (Gratton et al., 2018) but may depend on the task and the specific region. For example, Gruber et al. (2016) found reward modulation of memory related to task-induced connectivity changes, whereas we found that stable connectivity patterns predicted behavior. Regarding task differences, the Gruber et al. (2016) study tested incidentally encoded scene-object associations, with four possible scenes repeated many times across encoding. Two scenes were always low-value and two high-value. Performance on a forced-choice test asking which ofthe four scenes was previously associated with a given object (with “unsure or new” as a fifth option) could thus partially rely on remembering the object’s value, which is plausible given that object-scene memory was relatively close to chance. In contrast, the present study required intentional encoding of trial-unique object pairs, with trial value being unrelated to the memoranda. Regarding regional differences, Gruber et al. (2016) found midbrain connectivity changes that tracked behavior, which was partially replicated in our data. In contrast, we found stable connectivity patterns of other reward-related regions with memory regions to relate to behavior. Although speculative at this time, these commonalities and differences across findings open up a new avenue for inquiry regarding the factors that determine how stable vs. dynamic aspects of connectivity may relate to different aspects of cognition.

Complementary to our main questions of interest, we further conducted an exploratory analysis of potential functional differences between the anterior and posterior hippocampus. While anterior/posterior differences have been previously documented in various memory paradigms (Bowman & Zeithamova, 2018; Brunec et al., 2018; McKenzie et al., 2014; Poppenk et al., 2013), the evidence within the domain of motivated encoding is sparse and varying. For example, Murty et al. (2017) reported a stronger link between anterior hippocampal connectivity and behavior while Wolosin et al. (2013) found a reward modulation effect in the posterior hippocampus. We found anterior/posterior differences with respect to connectivity with distinct reward regions, however, the relationship to memory sensitivity to reward was comparable for the anterior and posterior hippocampus. These studies indicate that reward modulation of memory may not be strongly dissociated along the anterior/posterior axis.

In summary, the current study significantly expands our understanding of motivational influences on memory, newly demonstrating the role of stable, individual differences in connectivity in predicting how an individual’s memory is impacted by reward. The results also demonstrate a role for several reward-related regions in motivated encoding that have not been previously considered, highlighting the importance of both theoretically and empirically driven approaches in understanding brain-behavior relationships. More broadly, the work informs current theories on functional differentiation within the reward processing network and brings new evidence for the fruitfulness of utilizing individuals’ connectivity patterns in the study of cognition (Finn et al., 2015; Gratton et al., 2018).

Acknowledgments

This work was supported by a National Science Foundation CAREER Award BCS 1056019 (ARP), NIH-NIMH R01 MH100121 (ARP), and NIH-NIMH National Research Service Award F32 MH094085 (DZ). The authors declare no competing financial interests.