Part 2:Functional Connectivity Between Memory And Reward Centers Across Task And Rest Track Memory Sensitivity To Reward
Mar 17, 2022
for more information:ali.ma@wecistanche.com
Analysis of variance approach
To test whether connectivity patterns related to memory sensitivity to reward and whether this relationship is stable across task stages, the connectivity values were submitted to two repeated-measures ANOVAs. The first ANOVA only included rest data (pre-encoding, post- encoding), akin to prior work on resting-state connectivity (Gruber et al., 2016). The second ANOVA included all three task stages (pre-encoding rest, encoding task, post-encoding rest), with low-pass filtered rest time series for comparison with task time series. In addition to the task stage as a within-subject factor, both ANOVAs also included memory structure (hippocampus, PHC) and reward structure (ACC, midbrain, MPFC, OFC, VS) as within-subject factors and modulator status (modulator, nonmodulator) as a between-subjects factor.
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The following effects were relevant to our questions of interest: (1) the main effect and interactions of modulator status, testing whether connectivity patterns related to individual differences in memory sensitivity to reward; (2) the main effect and interactions ofthe task stage factor, testing the idea that connectivity patterns may be relatively stable across task and rest as well as track individual differences in memory sensitivity to reward; (3) the interactions of the reward region factor with the modulator status, testing whether all reward regions contribute similarly or differentially. Of note, the main effect of reward region and the main effect of memory region was not of interest as the overall functional connectivity value may depend on the physical distance between regions and size of a region, and may not be easily interpretable (Honey et al., 2009; Salvador et al., 2005). When an interaction was found, we followed up with an investigation of the locus of the interaction. While the report focuses on the effects of interest, full ANOVA results are reported in tables. Greenhouse Geisser corrections were used when appropriate, reported in the tables as “GG.” To validate that our findings were not driven by treating memory sensitivity to reward as a binary variable, we retested significant effects of interest from both ANOVAs using ANCOVA, with the continuous measure of behavioral reward modulation as a covariate.
Functional relationships among connections
Observing comparable or differential modulator effects across multiple reward ROIs in the ANOVAs provides one indication for unique or uniform contributions of reward regions to reward modulation of memory. To test more directly whether the reward regions are a part of the same functional network, we additionally examined their cross-correlational structure. We performed two principal component analyses: one on rest-only connectivity values (no low-pass filter to maintain information on high-frequency fluctuations), and one that included all of the connectivity values across task and rest (using low-pass filtered time series for the comparable task and rest pre-processing). Components were considered for further analysis when they explained at least 10% of the variance. For each considered component, we further tested the likelihood of obtaining such component by chance, using a comparison to a null distribution of components. To obtain the null distribution, we performed 10,000 simulated principal component analyses on data obtained by randomly shuffling connectivity values across participants, separately for each connection. The percent of variance explained by each component (first most informative, second-most informative, etc.) was then compared to the null distribution’s percent explained. The same results would be obtained by testing eigenvalues.
Loadings on each component were compared for the five reward ROIs using one-way ANOVA, and component scores were then related to behavioral reward modulation using multiple regression. Using dimensionality reduction before the multiple regression allowed us to test how underlying components, or potential networks of regions, contributed to the connectivity-behavior relationship while taking into account the collinearity between connectivity values and limiting in a data-driven manner the number of predictors considered.
Connectivity pattern classification
While traditional inference tests, such as analysis of variance, test the probability that observed differences between groups arose by chance alone, machine learning classification approaches allow us to quantify more directly how well the participants can be distinguished from one another based on their connectivity pattern. We used Support Vector Classification (SVC) to test the degree to which participants can be classified as either modulators or non- modulators based on their pattern of connectivity across the ten ROI connections (2 memory ROIs × 5 reward ROIs).
SVC was implemented using the “e1071” (Meyer, Dimitriadou, Hornik, Weingessel, & Leisch, 2017) statistical analysis package and conducted separately within each task stage. The default parameters for nu-classification were used (C = 1, ε= 0.1, γ= 0.1, no tuning) with a radial basis function kernel. We used a leave-one-subject-out cross-validation approach, training the model on N-1 subjects and then applying the trained classifier to predict the withheld subject’s modulator status. The process was repeated as each subject in turn was withheld from the training set and used to test the model. The accuracy for the model was recorded as the percentage of correct classifications. A permutation test was used to test for significance. We conducted 5,000 simulations, each time randomly shuffling the modulator status labels across participants and then computing the same leave-one-subject-out cross-validated classification accuracy as with the real data. The true classifier accuracy was compared to the distribution of the simulated classification accuracies to derive the probability of obtaining such accuracy by chance alone. Accuracy that occurred with probability less than p= 0.017 was considered significant, reflecting Bonferroni correction across three task stages for an overall alpha = 0.05.
To verify the results were not driven by the median split approach, Support Vector Regression (SVR) was used to predict the continuous measures of behavioral reward modulation (BRM score) for each participant from connectivity measured at each task stage. The same statistical package, default parameters, and leave-one-subject out cross-validation
the approach was used for SVR as were used for SVC. The predicted BRM values for each subject were then correlated with the observed BRM values to assess whether the individual
differences in connectivity patterns contain information about individual differences in behavioral reward modulation of memory. We employed Bonferroni corrections for the three correlations (alpha = 0.05/3 = 0.017).
Complementary connectivity analyses
In addition to the main questions of interest, the current study provides data suitable to address questions from prior studies on reward modulation of memory. We conducted two sets of exploratory analyses that maintain the focus on connectivity and may be informative for the readers, even though they do not directly address the main goals of the study.
Correlations between connectivity changes and behavior—The ANOVA, PCA,
and machine learning approaches are well suited for testing the role of a broad set of reward regions and the connectivity fingerprint hypothesis, especially for the larger set of related connections considered here. In contrast, prior studies on reward modulation of memory have typically focused on single connections and learning-related effects, reporting first-order correlations relating pre-to-post encoding connectivity increases. While a disproportionate role of post-encoding rest could be indicated by a significant modulator by task stage interaction in our ANOVA, we also wanted to generate data directly comparable to prior studies. We thus additionally computed pre-to-post connectivity changes for each connection and correlated them with BRM. Because increased dopamine availability in the medial temporal lobe may enhance encoding in general (Duncan et al., 2014; Lisman et al., 2011), we also correlated the connectivity values with overall recall rates for each participant.
Anterior and posterior differences within the hippocampus—Previous work suggests
there are functional differences between the anterior and posterior portions of the hippocampus (Brunec et al., 2018; McKenzie et al., 2014; Poppenk, Evensmoen, Moscovitch, & Nadel, 2013). In the context of reward-motivated learning, however, evidence for differential contributions of the anterior and posterior hippocampus is lacking or conflicting (Murty et al., 2017; Wolosin et al., 2013). We have performed exploratory analyses of anterior/posterior hippocampal connectivity patterns to test whether their connectivity patterns or connectivity changes are differentially related to behavior in our paradigm.
The middle slice of each participant’s hippocampus ROI was used as a boundary for the anterior and posterior divisions. For participants that had an odd number of slices in their hippocampus ROI, the middle slice was assigned to the posterior portion. The ROIs were then used to extract the time series during each rest and task scan. Connectivity between the anterior and posterior hippocampus with each reward region was measured using the procedures outlined above. Functional differences between anterior and posterior hippocampus were tested using repeated-measures ANOVA with hippocampal ROI (anterior, posterior) × task stage (pre-encoding, encoding, post-encoding) × reward ROI (ACC, midbrain, MPFC, OFC, VS) as within-subject factors and modulator status as a between-subjects factor. Of main interest was the interaction between hippocampal ROI and modulator status, testing whether anterior and posterior hippocampus differentially related to
reward modulation of memory.
Results
Behavioral results
Mean overall cued recall performance was 0.48 (SD= 0.19). A 2 (reward cue visual form) × 3 (reward cue value) repeated measures ANOVA revealed a marginally significant effect of reward value (F(1.18,27.03) = 3.86, p= 0.054, η2p= 0.14, GG), with a significant quadratic (F(1,23) = 9.93, p= 0.004, η2p= 0.30) rather than a linear effect (F(1,23) = 1.97, p= 0.174). Follow-up pairwise comparisons revealed that the quadratic effect was driven by greater recall on dollar trials (M= 0.53, SD= 0.20; t(23) = 2.41, p= 0.024), and unexpectedly, penny trials (M= 0.47, SD= 0.22; t(23) = 2.45, p= 0.022) compared with dime trials (M= 0.44, SD= 0.22). The difference between dollar and penny trials did not reach significance (t(23) = 1.40, p= 0.174). There was no main effect of visual form (F(1,23) = 0.04, p= 0.840, η2p= 0.002) nor an interaction between form and value (F(2,46) = 1.66, p= 0.202, η2p= 0.07). Thus, accuracies were collapsed across visual form and used for all subsequent analyses. Cued recall rates for each reward value and form condition are presented in Fig.3a.
A separate behavioral sample (n = 20) revealed a significant main effect of value (F(1.23, 27.6) = 14.1, p= 0.001, GG), comparably described as linear (F(1,19) = 15.5, p= 0.001) or quadratic (F(1,19) = 10.8, p= 0.004). Similar to the fMRI sample, cued recall accuracy was greater for dollar trials (M= 0.61, SD= 0.19) than for dime trials (M= 0.44, SD= 0.21; t(19) = 3.83, p= 0.001). Unlike the fMRI sample, the behavioral sample showed a memory advantage for dollar trials compared to penny trials (M= 0.44, SD= 0.19; t(19) = 3.94, p = 0.001) and no differences between penny and dime trials (t(19) = 0.17, p = 0.87).
While the U-shaped pattern of recall accuracies in the fMRI sample was unexpected and did not replicate in the separate behavioral sample, non-linear reward effects are plausible (Elliott, Newman, Longe, & Deakin, 2003). For example, penny trials may have been perceived as a loss relative to the (neutral) dime trials, making them more salient for encoding (Bartra, McGuire, & Kable, 2013; Seymour & McClure, 2008; Shigemune, Tsukiura, Kambara, & Kawashima, 2014; Tversky & Kahneman, 1981). Because the difference between dollar and penny trials was not significant and because both penny and dollar may have increased salience for individuals sensitive to reward, we instead used the memory advantage of a dollar over dime trials (replicated across both behavioral and fMRI samples) as a measure of individual differences in memory sensitivity to reward. The raw dollar minus dime difference scores ranged from −0.25 to 0.75 (median of 0.07) and were not significantly correlated with the overall accuracy (Fig. 3b), suggesting that reward modulation of memory affected which events are preferentially remembered rather than providing an overall memory advantage. Because the raw difference scores were skewed by an outlier (>3 SD from the mean), we used a rank order of these scores in all subsequent analyses when correlating memory sensitivity to reward with connectivity measures. We refer to the dollar-dime difference score as a raw behavioral reward modulation (raw BRM)score and the rank-order measure used for all subsequent analyses as a behavioral reward modulation (BRM) score.
For visualization and analysis purposes, we also constructed a dichotomized measure of reward modulation of memory using a median split of BRM scores. This approach created two groups of participants that we refer to as modulators (sensitive to reward) and non- modulators (insensitive to reward). We performed confirmatory analyses to validate that the median split of participants yielded sensible groupings. Figure 3c shows cued recall accuracy per value, separately for each group. There was no effect of reward value in nonmodulators (one-way ANOVA F(1.15,12.7) = 1.36, p= 0.273, GG), with raw BRM scores (dollar-dime difference) not different from zero (M= −0.02, t(11) = −0.98, p= 0.348), confirming that memory performance in this group was not significantly affected by reward value. In contrast, modulators showed an effect of reward value (one-way ANOVA F(1.18,13.01) = 8.69, p= 0.009, η2p= 0.44, GG), with greater accuracy for dollar trials than for dime trials (i.e., significant raw BRM scores; M= 0.21; t(11) = 3.59, p= 0.004), and greater accuracy for dollar trials than for penny trials (t(11) = 2.43, p= 0.033). Thus, the median split generated two sensible groups of participants that differ in their memory sensitivity to reward.
ANOVA results
Rest-only ANOVA—We first addressed the relationship between rest connectivity and memory sensitivity to reward in a repeated-measures ANOVA with rest period (pre-encoding, post-encoding), memory ROI (hippocampus, PHC), and reward ROI (ACC, midbrain, MPFC, OFC, VS) as within-subjects factors and modulator status as a between-subjects factor. The rest time series were not low-pass filtered for this analysis, as such a preprocessing step is not ordinarily applied during rest time-series analyses as it may remove meaningful high-frequency fluctuations. All connections are depicted in Fig. 4a, and the complete ANOVA results are reported in Table 1.
Modulator status was marginally significant (p= 0.051), with modulators (M= 0.36, SD= 0.10), demonstrating numerically greater hippocampus/PHC-reward network connectivity than nonmodulators (M= 0.29, SD= 0.06). Modulator status significantly interacted with reward structure. This interaction was driven by greater hippocampus/PHC connectivity with ACC, OFC, and VS in modulators than non-modulators (all t> 2.15, all p< 0.045), with no effect ofmodulator status in hippocampus/PHC-midbrain and hippocampus/PHC-MPFC connectivity (both t< 1.4, p> 0.18). When reward modulation of memory was treated as a continuous measure using ANCOVA, the results were similar but weaker. The main effect of BRM (r(22) = 0.35; F(1,22) = 3.12, p= 0.091, η2P= 0.12) remained marginally significant, but the interaction between reward structure and BRM did not (F(2.83,62.22) = 1.99, p= 0.128, GG).
The ANOVA additionally revealed a main effect of rest period, with connectivity increasing from the pre-encoding (M= 0.30, SD= 0.10) to post-encoding rest scan (M= 0.36, SD= 0.11). The rest period did not interact with modulator status (p> 0.6) or BRM in the ANCOVA (p> 0.3), indicating that although the overall connectivity increased from pre-encoding to post-encoding, its relationship to behavior did not change significantly.
