Part 1:Functional Connectivity Between Memory And Reward Centers Across Task And Rest Track Memory Sensitivity To Reward
Mar 17, 2022
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Lea E. Frank1, Alison R. Preston2, Dagmar Zeithamova1
1Department of Psychology, University of Oregon, 1227 University Street, Eugene, OR 97403, USA
2Center for Learning and Memory and Department of Psychology, The University of Texas at Austin, Austin, TX 78712, USA
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Abstract
External motivation, such as a promise of future monetary reward for remembering an event, can affect which events are remembered. Reward-based memory modulation is thought to result from encoding and post-encoding interactions between dopaminergic midbrain, signaling reward, and hippocampus and parahippocampal cortex, supporting episodic memory. We asked whether hippocampal and parahippocampal interactions with other reward-related regions are related to reward modulation of memory and whether such relationships are stable over time. Individuals’ memory sensitivity to reward was measured using a monetary incentive encoding task in which a cue indicated potential monetary reward (penny, dime, or dollar) for remembering an upcoming object pair. Functional connectivity between memory and reward regions was measured before, during, and following the task. Reward-related regions of interest were generated using a meta-analysis of existing studies on reward and included ventral striatum, medial and orbital prefrontal cortices, and anterior cingulate cortex, in addition to the midbrain. The results showed that connectivity between memory and reward regions tracked individual differences in reward modulation of memory, irrespective of when connectivity was measured. Connectivity patterns of the anterior cingulate, orbitofrontal cortex and ventral striatum covaried together and tracked behavior most strongly. These findings implicate a broader set of reward regions in reward modulation of memory than considered previously and provide new evidence that stable connectivity patterns between memory and reward centers relate to individual differences in how reward impacts memory.
Keywords
Episodic memory; Functional connectivity; Hippocampus; Motivation; Reward; Prefrontal cortex
Why are some events remembered and others forgotten? Reward-based motivation is one factor that impacts which events are remembered. The modulation of memory by rewards is thought to result from reward-related activation ofthe dopaminergic midbrain and the Dagmar Zeithamova dasa@uoregon.edu.
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projection of dopamine into the hippocampus, which facilitates long-term potentiation and memory formation (Lisman & Grace, 2005; Lisman, Grace, & Duzel, 2011; Shohamy & Adcock, 2010). Consistent with this model, several functional MRI studies have documented increased activation in the midbrain and ventral striatum, accompanied by univariate and
multivariate signals reflecting reward in the hippocampus and parahippocampal cortex (PHC) within the medial temporal lobe (Adcock, Thangavel, Whitfield-Gabrieli, Knutson, & Gabrieli, 2006; Gruber, Ritchey, Wang, Doss, & Ranganath, 2016; Wittmann et al., 2005; Wolosin, Zeithamova, & Preston, 2012, 2013). A common finding in these studies is that individuals differ in the degree to which their memory is affected by extrinsic rewards (Adcock et al., 2006; Gruber et al., 2016; Wolosin et al., 2012, 2013).
Individual differences in memory sensitivity to reward have been related to both phasic and tonic interactions between the midbrain and medial temporal lobe (Shohamy & Adcock, 2010). Adcock et al. (2006) first found that task-related activation in the hippocampus, PHC, midbrain, and ventral striatum were correlated participants. Wolosin et al. (2012) showed background hippocampal-midbrain interactions across both encoding and retrieval phases track memory sensitivity to reward. Other studies focused on learning-induced connectivity increases, relating them to reward-motivated memory (Gruber et al., 2016; Murty, Tompary, Adcock, & Davachi, 2017), although hippocampal-midbrain interactions may relate to associative memory in general (Duncan, Tompary, & Davachi, 2014; Tompary, Duncan, & Davachi, 2015).
Across many memory studies, the emphasis has been on connectivity during task performance or connectivity changes after learning as primary predictors of behavior (Gruber et al., 2016; Murty et al., 2017; Tambini, Ketz, & Davachi, 2010; Tompary et al., 2015). In contrast, other fields have focused on resting connectivity patterns, given that individual differences of intrinsic connectivity can remain relatively stable across time and tasks (Finn et al., 2015; Gratton et al., 2018), predicting individual differences in cognition (Finn et al., 2015; Gerraty, Davidow, Wimmer, Kahn, & Shohamy, 2014; Poole et al., 2016; Wang et al., 2010). Therefore, we wanted to bridge these approaches and test whether there are stable interactions between memory and reward regions that track individual differences in memory sensitivity to reward, irrespective of when those interactions are measured. For example, even in the absence of a task, the strength of connectivity between hippocampus, midbrain, and ventral striatum varies across individuals (Kahn & Shohamy, 2013). Because these regions are involved in memory and reward processes, these task-independent interactions may relate to individual differences in memory sensitivity to reward. However, the idea of an individual’s connectivity “fingerprint”—used in a broad sense referring to connectivity patterns differentiating groups and tracking performance (Gratton et al., 2018; Wang et al., 2010) rather than identifying specific individuals (Finn et al., 2015)—has not yet been tested in the area of reward modulation of memory.
Theoretical perspectives, particularly the Lisman and Grace (2005) model, have emphasized the role of the dopaminergic midbrain in motivated learning. Influenced by this model, neuroimaging studies on memory sensitivity to reward have typically focused on the midbrain as the primary reward region of interest (Adcock et al., 2006; Gruber et al., 2016; Wittmann et al., 2005; Wolosin et al., 2012). However, other reward-related regions are likely to contribute to motivational effects on memory. The ventral striatum is believed to play a central role in integrating signals between the hippocampus and midbrain (Lisman & Grace, 2005; Miendlarzewska, Bavelier, & Schwartz, 2016), is recruited during motivational encoding (Adcock et al., 2006; Wittmann et al., 2005), and has been shown to interact with the hippocampus both during rest (Kahn & Shohamy, 2013) and task performance (Adcock
et al., 2006; Camara, Rodriguez-Fornells, & Münte, 2009; Kafkas & Montaldi, 2015). Prefrontal regions, including the orbitofrontal cortex (OFC) and medial prefrontal cortex (MPFC), also interact with the hippocampus and PHC (Blessing, Beissner, Schumann, Brünner, & Bär, 2016; Gerraty et al., 2014; Murty, LaBar, & Adcock, 2016) and have been implicated in various reward-related processes (Amiez, Joseph, & Procyk, 2006; Elliott, Agnew, & Deakin, 2008; Kable & Glimcher, 2007). However, because ofthe theoretical emphasis on the midbrain, and to a lesser degree striatum, it is unknown whether other reward-related regions also contribute to reward modulation of memory.
The current study had two main goals. First, we sought to determine the role of a broader network of reward-related regions in mediating reward modulation of memory. Reward-related regions of interest were independently derived based on their involvement in reward processing using an automated meta-analysis tool Neurosynth (Yarkoni, Poldrack, Nichols, Van Essen, & Wager, 2011), irrespective of their prior implication in reward modulation of memory. Second, we aimed to determine to what extent individual differences in memory sensitivity to reward relate to individual differences in connectivity between memory and reward centers and whether such a relationship may exist irrespective of when connectivity is measured. To evaluate the stability of connectivity patterns and their relationship to behavior, interactions between hippocampus and PHC with a network of reward-related regions were measured using functional MRI during a monetary incentive encoding task (Adcock et al., 2006), as well as during rest scans before and after the task. The pattern of connectivity for each participant was related to their memory sensitivity to reward, defined as memory advantage for high-value trials, using analysis of variance and machine-learning approaches. A separate report from this data set, focusing on hippocampal and PHC task-related activation patterns and how they represent reward, has been published previously (Zeithamova, Gelman, Frank, & Preston, 2018).
Materials and methods
Participants
Thirty-four healthy, English-speaking volunteers were enrolled in this study. Data from nine participants were excluded for excessive head motion during task scans (framewise displacement >1 mm in at least 50-time points in more than 1 run; 4 participants), scanning interruptions (3 participants), or missing data (2 participants). An additional participant was excluded due to excessive head motion during a rest scan (>50% of time points removed during scrubbing). The remaining 24 subjects (18 females, ages 18–31, mean age = 22) were included in the connectivity analyses. Subjects received $40 for participation and up to $55.50 bonus for their memory performance. The study was approved by the Institutional Review Board of The University of Texas at Austin, and all participants provided written consent. A separate sample of20 participants (5 females, ages 18–24 years, mean age 19 years) completed the same task but were not scanned.
Behavioral procedures
Following the consent procedure, participants were instructed on the task and completed five practice trials. Next, they were screened for MRI, changed into scrubs, and were positioned into an MRI magnet. The scanning session started with the acquisition of anatomical scans, followed by functional scans. Participants completed a pre-encoding rest scan (6 minutes), a motivated encoding task across five event-related runs (9 minutes each), and a post-encoding rest scan (6 mins), with 1–2 minutes between all scans (Fig. 1a). During rest scans, participants were instructed to keep their eyes open, with a blank screen in front of them. During the motivated encoding task, participants were instructed to intentionally encode 150 pairs of common objects, each preceded by a cue (in pictorial or word form), indicating a reward value (penny, dime, or dollar) that they may earn if they remembered the object pair in a later memory test (Fig. 1b). The object pairs were drawn from a set of 300 color photographs of objects and randomly assigned to one ofthe six reward-cue conditions, resulting in 25 pairs per condition. Participants were informed that they would receive a cash bonus indicated by the reward cue for correctly recalling the associations in a cued-recall task that immediately followed the scanning session. Trials from all conditions were presented in a randomized order, with a balanced number of presentations in each ofthe five encoding runs. A self-paced cued recall test was completed following the scanning session, approximately 20–30 minutes after the post-encoding rest. During each test trial, participants were shown the left object of each pair and asked to name aloud the associated object, followed by a source memory test during which participants selected the reward cue that preceded that object (Fig. 1c). Participants were not informed that they would be tested on the cue identity prior to the test. Source memory for cue identity was at chance in the fMRI sample (Zeithamova et al., 2018) and is not considered further in this report.
For each participant, the mean proportion of correctly recalled associations following each of the six possible cues was computed. A 2 (form: picture, word) × 3 (value: penny, dime, dollar) repeated measures ANOVA examined the within-subjects effect of reward value and form on memory. For significant effects, follow-up pairwise comparisons were conducted to determine the differences between the mean accuracies of each condition. The behavioral data were used to index individual differences in reward modulation of memory. Because the behavioral effect of reward was found to be U-shaped in the fMRI sample, we used the difference between the dollar and dime trial accuracy as a measure of memory sensitivity to reward. We refer to this score as the behavioral reward modulation (BRM) score. Additionally, a median split of Brm scores sorted participants into two groups: modulators (those who demonstrated memory sensitivity to reward), and nonmodulators (those whose memory scores were insensitive to reward). A confirmatory analysis ofthe reward effect on memory was performed within each group to verify that “modulators” indeed showed a memory advantage for dollar trials while “nonmodulators” did not. We refer to this dichotomized measure of memory sensitivity to reward as a modulatorstatus.
fMRI acquisition
Functional and structural MR images were collected at the Imaging Research Center at the University of Austin at Texas using a 3T Siemens Skyra MRI scanner. Functional images were collected in 72 oblique axial slices, approximately 20 degrees from the AC-PC line, using echo-planar imaging sequences with multiband acceleration factor = 3, GRAPPA factor = 2, TR = 2,000 ms, TE = 31 ms, flip angle = 73°, 128 × 128 × 72 matrix resulting in 1.7-mm isotropic voxels. Using the same parameters, two 6-minute resting-state fMRI scans were conducted, one preceding and one following the encoding task. A T1weighted high-resolution MPRAGE anatomical image (256 × 256 × 192 matrices, 1-mm isotropic voxels) was collected. An additional T2-weighted image was collected in an oblique coronal plane perpendicular to the hippocampal axis (TR = 13,150 ms, TE = 82 ms, 512 × 60 × 512 matrices, 0.4- × 0.4-mm in-plane resolution with 1.5-mm slices, no gap).
Regions of interest
Because prior studies on motivated encoding have primarily focused on the midbrain, little is known about how other reward-related regions may affect memory sensitivity to reward. Our goal was to include a wider reward-related network in the current investigation, irrespective of whether they have been previously implicated in reward modulation of memory. To obtain ROIs related to reward processing, a meta-analysis of 671 studies including the term “reward” was collected from the Neurosynth database (http://neurosynth.org). We used the “reverse inference” map (currently referred to as the “association test”), which displays regions that preferentially activate in studies that include the term “reward” compared with studies that do not include the term “reward” and as such is considered diagnostic ofthe term in question (Yarkoni et al., 2011). Because the default Neurosynth threshold (FDR p< 0. 01) yielded large clusters with multiple peaks in anatomically distinct regions, we further thresholded the maps with a voxel-wise threshold ofZ = 5.3 to obtain clusters that did not extend across multiple anatomical regions. This meta-analysis resulted in five reward-related ROIs that centered on the anterior cingulate cortex (ACC), midbrain, medial prefrontal cortex (MPFC), orbitofrontal cortex (OFC), and ventral striatum (VS). Clusters centered on the midbrain and VS were disproportionately larger than the prefrontal ROIs and extended beyond the anatomical boundaries of their respective regions, thus these clusters were further reduced to the top 500 voxels. The localization ofthe resulting five reward-related ROIs in the standard space is presented in Fig. 2a. The reward-related ROIs were reverse transformed from standard space to native space of each participant using FLIRT, a part of FSL (http://www.fmrib.ox.ac.uk/fsl). Finally, the reward ROIs were resampled to the functional space of the participant to serve as masks for extracting time series.
Given the number of reward-related regions, only the hippocampus and PHC were selected as memory regions of interest to limit the total number of connections considered. Hippocampus and PHC were selected as our a prior memory ROIs, because they have been consistently implicated in studies of reward effects on memory (Gruber et al., 2016; Wolosin et al., 2012, 2013). To obtain unbiased ROIs, we defined hippocampus and PHC anatomically in each participant’s native space. We did not use a functional definition (e.g., Neurosynth) as it was not apparent whether standard memory voxels in these regions must be also most relevant for memory modulation by reward. However, the Neurosynth“memory” maps do cover essentially the whole anatomical hippocampus and PHC and would thus yield the same results. Anatomical ROIs were obtained by cortical parcellation and subcortical segmentation ofthe T1 anatomical scan via Freesurfer (https:// surfer.nmr.mgh.harvard.edu). The T1 anatomical scan was then coregistered to the first functional scan using Advanced Normalisation Tools), and the coregistration parameters were applied to the Freesurfer segmentation. Finally, the participant-specific, Freesurfer-defined hippocampus and PHC were transformed into the space oftheir functional scans to be used as masks for extracting hippocampal and PHC time series. Hippocampus and PHC ROIs are presented for an example subject in Fig. 2b.
Deriving functional connectivity measures
Preprocessing was conducted using tools from FSL version 5.0 and ANTS. The functional and anatomical images were brain extracted using BET. Functional images were motion-corrected within each run using FLIRT from FSL, realigned across runs to the first functional image using ANTS, and high-pass filtered (128-s cutoff). Functional connectivity was measured during the pre-encoding rest scan, the five encoding scans, and the post- encoding rest scan using similar processing procedures. As connectivity measures may be affected by noise in the BOLD signal evoked by motion and physiological processes (Murphy, Birn, & Bandettini, 2013; Power, Barnes, Snyder, Schlaggar, & Petersen, 2012), we followed the pre-processing procedures outlined by Power et al. (2012) to remove noise-related signal fluctuations. First, time courses were extracted for cerebrospinal fluid (CSF), white matter (WM), and the whole brain, because signal changes in these regions provide a good proxy to signal changes driven by motion and other confounds. The six realignment motion parameters, framewise displacement (FD), and global signal change (DVARS) also were extracted. We then created a “scrubbing” mask using the time series for FD and DVARS. Time points that exceeded either threshold (FD > 0.5 mm or DVARS > 0.5%) were marked for removal, as was a one-time point before and two-time points after (Power et al., 2012). Additionally, the first two time points in each scan were removed. The scrubbing masks were applied to the time courses for each subject, removing on average approximately 6% ofthe time points from the pre-encoding scan, 9.5% from the post-encoding scan, and an average of 8% of time points across all five encoding scans.
To extract the background connectivity during the encoding scans, we low-pass filtered the encoding time series, removing signal at or above the task frequency (task frequency = 0.056 Hz, filter threshold = 0.045 Hz). The low-pass filter removed task-related fluctuations while keeping the low-frequency (background) signals that are more reflective of intrinsic activity (Newton, Morgan, Rogers, & Gore, 2011; Tambini, Rimmele, Phelps, & Davachi, 2016). Because low-pass filtering also removes high-frequency noise, leading to higher connectivity estimates (Van Dijk et al., 2010), we additionally low-pass filtered the rest time-series at the same frequency when comparing connectivity across task and rest. Low-pass filtering was performed after computing FD and DVARS but before scrubbing of problematic timepoints.
Connectivity measures were obtained by partial correlations ofthe pre-processed, scrubbed time series between each of the memory ROIs and each of the five reward-related ROIs, controlling for WM, CSF, whole-brain signal, motion parameters, and their derivatives. The resulting Pearson’s coefficients from the partial correlation analysis were Fisher z transformed to conform to the assumptions of normality before being submitted to further analyses. For the encoding scans, connectivity was measured and Fisher z transformed within each run. The normalized connectivity values were then averaged across the five encoding scans to produce a single measure of background connectivity during encoding.
