Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Feb 1.
Published in final edited form as: Hippocampus. 2017 Nov 17;28(2):76–80. doi: 10.1002/hipo.22812

Increased hippocampus to ventromedial prefrontal connectivity during the construction of episodic future events

Karen L Campbell 1, Kevin P Madore 2, Roland G Benoit 3, Preston P Thakral 4, Daniel L Schacter 4
PMCID: PMC5777865  NIHMSID: NIHMS921180  PMID: 29116660

Abstract

Both the hippocampus and ventromedial prefrontal cortex (vmPFC) appear to be critical for episodic future simulation. Damage to either structure affects one’s ability to remember the past and imagine the future, and both structures are commonly activated as part of a wider core network during future simulation. However, the precise role played by each of these structures and, indeed, the direction of information flow between them during episodic simulation, is still not well understood. In this study, we scanned participants using functional magnetic resonance imaging (fMRI) while they imagined future events in response to object cues. We then used dynamic causal modeling (DCM) to examine effective connectivity between the left anterior hippocampus and vmPFC during the initial mental construction of the events. Our results show that while there is strong bidirectional intrinsic connectivity between these regions (i.e., irrespective of task conditions), only the hippocampus to vmPFC connection increases during the construction of episodic future events, suggesting that the hippocampus initiates event simulation in response to retrieval cues, driving activation in the vmPFC where episodic details may be further integrated.

Keywords: imagination, hippocampus, ventromedial prefrontal cortex, fMRI, dynamic causal modeling

Mounting evidence indicates that imagining future experiences (episodic simulation) and remembering past experiences (episodic memory) rely on similar constructive processes that reinstate and recombine information (for a recent review, see Schacter et al., 2017b). In line with this view, both past and future events activate the same core network of brain regions, including the medial temporal lobes (MTL) extending to posterior cingulate/retrosplenial cortex, ventromedial prefrontal cortex (vmPFC), dorsomedial prefrontal cortex, and lateral parietal and temporal areas (Benoit and Schacter, 2015). Although the unique contribution made by each of these regions to episodic simulation remains unclear, neuropsychological and neuroimaging studies have focused in particular on two regions: the hippocampus and vmPFC (for discussion, see Bertossi et al., 2015; Kurczek et al., 2015; Schacter et al., 2017a). Neuroimaging studies suggest that hippocampal activity tracks with the level of retrieval or recombinatorial demand (cf. Gaesser et al., 2013; Thakral et al., 2017). In contrast, vmPFC activity is stronger when the elements of a simulated event are more familiar (Szpunar et al., 2009; Benoit et al., 2014) or self-relevant (D’Argembeau et al., 2010). Thus, the vmPFC may play a critical role in incorporating one’s sense of “self” into simulated events and integrating activated representations with existing knowledge structures or schemas (Van Kesteren et al., 2012; Benoit et al., 2014; Demblon et al., 2016).

Importantly, the hippocampus and vmPFC do not function in isolation: They form part of the same “medial temporal lobe subsystem” within the larger default/core network (Andrews-Hanna et al., 2010b; Campbell et al., 2013), and dynamically interact to support memory and imagination functions (Zeithamova et al., 2012; Ritchey et al., 2015; Brown et al., 2016). However, measures of functional connectivity, or the degree to which activity in different brain regions correlate over time, can only tell us part of the story. A more thorough understanding of how these regions interact to give rise to simulated events requires a measure of effective connectivity, or the causal influence of one region on another (Friston, 2011). Understanding the dynamics behind hippocampal-vmPFC interactions during episodic future thinking may help further elucidate the unique contribution made by each of these regions.

To this end, we scanned participants (N = 32, 20 females; mean age = 21.0 years, SD = 2.38) using functional magnetic resonance imaging (fMRI) while they imagined future events in response to object word cues or performed a non-episodic control task (see Figure 1; data from Madore et al., 2016, see that paper for fMRI acquisition and prepocessing details). Peak regions (imagine > control; p < .05, FWE corrected) were identified in the vmPFC [x = 0, y = 50, z = −14] and left anterior hippocampus [x = −24, y = − 16, z = −18] and subject-specific time series were extracted separately for each run (using a 6-mm radius sphere centered at individual maxima within 6mm of the group peak, adjusted for effects of interest). We opted for the left anterior hippocampus because this region is consistently related to episodic simulation (e.g., Addis et al., 2007; Martin et al., 2011) and more recently, has been shown to be modulated by a specificity induction known to increase episodic retrieval (Madore et al., 2016). Since our primary interest here was effective connectivity during event construction in general, rather than the effect of induction, we collapsed across the specificity and control inductions used by Madore and colleagues (2016) to provide 6 runs of task data per subject (thus maximizing our power)1. Further, we focused on the construction phase, rather than the elaboration phase, because this portion of the trial places the highest demands on retrieval and recombination, as indicated by the finding that effects of the episodic specificity induction were observed only during construction (Madore et al., 2016).

Figure 1.

Figure 1

Open in a new tab

Overview of experimental procedure. On Imagine trials, participants were instructed to silently generate a novel future event that related to the cue, that was detailed, plausible, specific to one place and time, and viewed from a field perspective. They pressed a button once the event was constructed and continued to elaborate for the remainder of the trial. On Object trials, participants were instructed to silently generate two objects that related to the object cue and put these objects into a size sentence (e.g., “Board is larger than hammer is larger than nail”). They pressed a button once the sentence was constructed and then continued to think of definitions for each object.

Dynamic causal modeling (DCM) was used to examine effective connectivity between the vmPFC and hippocampal nodes during initial event construction. The advantage of DCM is that it not only provides a measure of ongoing (or endogenous) effective connectivity between regions, but also a measure of how these directed connections are modulated by task demands (Friston et al., 2003; Stephan et al., 2010). The user specifies a number of (anatomically feasible) neuronal models and these are tested against the observed data, yielding three sets of parameters: 1) endogenous parameters reflecting the strength of context-independent connectivity between regions (in this case, connectivity between the hippocampus and vmPFC throughout the run, irrespective of condition), 2) modulatory parameters reflecting context-dependent changes in connectivity between regions (in this case, connections altered on imagination trials), and 3) extrinsic parameters reflecting the influence of driving inputs on the system (in this case, all trial and rating phase onsets). Bayesian Model Selection (BMS) is used to determine which model, or family of models (sharing some common element), offers the best fit to the data (Penny et al., 2010). Given previous demonstrations of information flow from the hippocampus to vmPFC in other related paradigms (e.g., McCormick et al., 2015; Place et al., 2016), as well as extensive work showing that the hippocampus acts as an index to episodic details stored elsewhere in the neocortex (for a recent review, see Moscovitch et al., 2016), we hypothesized that the hippocampus would drive activation in the vmPFC during the construction of episodic future events.

Our model space consisted of four families of models, each differing in the modulatory effect of imagination (modeled as events of zero duration 2s after cue onset on imagination trials; see Figure 2). All models had bidirectional intrinsic connections between the hippocampus and vmPFC, as well as within-region inhibitory autoconnections. Within each family, driving inputs (corresponding to all trial and rating phase onsets, modeled as events of zero duration) could enter through the hippocampus, vmPFC, or both regions. Models were estimated separately for each participant and each run, and the estimated models were then submitted to a random-effects BMS analysis at the family level to determine the winning family. The results showed that Family 3, with imagination affecting connectivity in both directions, offered the best fit to the data with an exceedance probability (xp; or the probability that one model/family is more likely than any other) greater than 0.99.

Figure 2.

Figure 2

Open in a new tab

Model specification. Models are divided into 4 families, differing in the modulatory effect of imagination (shown along top and with bold arrows). Within each family, models differ based on where driving inputs enter the system (shown along left-hand side). Exceedance probabilities (xp) for BMS 1 (i.e., at the family level) are shown for each family at the bottom of the figure, whereas xp’s for models within the winning family are shown in the column for Family 3.

We next compared the models within Family 3 to identify a winning model. Model 8 (with driving inputs entering through the vmPFC alone) and model 9 (with driving inputs entering through both the vmPFC and hippocampus) could best account for the data (model 8: xp = 0.43, mean variance explained = 6.63%; model 9: xp = 0.57, mean variance explained = 10.22%), though there was no clear winner between the two.

Bayesian model averaging (BMA) was then used to calculate weighted model parameters for the winning models within Family 3 (i.e., weighted by the posterior probability or evidence for each model; Stephan et al., 2010). While the hippocampus and vmPFC showed significant bidirectional intrinsic connectivity throughout the scan (for statistics, see Table 1), only the hippocampus to vmPFC connection increased during event construction, t(31) = 3.67, p < .001, resulting in significant positive effective connectivity overall (i.e., the sum of the intrinsic and modulatory parameters), t(31) = 5.17, p < .001 (see Figure 3). In contrast, vmPFC to hippocampus connectivity decreased slightly, though not significantly, during event construction, t(31) = 1.13, p = .27, resulting in non-significant vmPFC-hippocampus coupling overall, t(31) = .42, p = .68 (see Figure 3).

Table 1.

BMA Results: Weighted Model Parameters

Mean (Hz) SD (Hz) t (31) p
Intrinsic connectivity [A]
 vmPFC to Hip 0.16 0.11 8.73 < .00001
 Hip to vmPFC 0.37 0.24 8.71 < .00001
Modulation by imagination [B]
 Hip to vmPFC 1.08 1.66 3.67 < 0.001
 vmPFC to Hip −0.26 1.32 1.13 0.27
Driving input [C]
 All Onsets Hip 0.02 0.20 0.58 0.56
 All Onsets vmPFC −0.19 0.34 3.19 <0.01
Rating phase inputs [C]
 Imag Rate Hip −0.07 0.14 2.65 0.01
 Obj Rate Hip 0.03 0.12 1.47 0.15
 Imag Rate vmPFC −0.25 0.40 3.54 .001
 Obj Rate vmPFC 0.22 0.29 4.19 <0.001
Open in a new tab

Note. Results shown in bold survive FDR correction. vmPFC = ventromedial prefrontal cortex, Hip = hippocampus, Imag = Imagine trials, Obj = Object trials.

Figure 3.

Figure 3

Open in a new tab

Graphic illustration of weighted model parameters. Dots represent individual parameter estimates, with boxplots overlaid. Total connectivity = intrinsic connectivity + modulation by simulation.

Taken together, these results suggest that on average there is strong bidirectional coupling between the hippocampus and vmPFC, but only the hippocampus to vmPFC connection increases during initial event construction. This fits well with previous work showing that activity in the hippocampus precedes that in the mPFC in rats during context-guided retrieval (Place et al., 2016)2 and in humans during autobiographical memory construction (McCormick et al., 2015). However, we cannot conclude from these results that the hippocampus is somehow more critical to episodic simulation than the vmPFC, which some have suggested (Kurczek et al., 2015), as clearly the hippocampus outputs to the vmPFC. Rather, it seems that the hippocampus initiates retrieval of episodic details, which are then integrated within the vmPFC, and these integrated representations may subsequently constrain further retrieval by the hippocampus (for a model of bidirectional hippocampal-mPFC interactions, see Preston & Eichenbaum, 2013). Alternatively, retrieved episodic details may be conveyed to the vmPFC, where they activate associated schematic representations that may then constrain further retrieval and construction processes (Van Kesteren et al., 2012; Benoit et al., 2014).

This study contributes to a growing body of work suggesting that hippocampal-vmPFC interactions are critical to a number of memory and imagination functions (Andrews-Hanna et al., 2010a; Zeithamova et al., 2012; Ritchey et al., 2015; Brown et al., 2016). Going forward it will be important to determine the cognitive factors that influence effective connectivity between these regions. For instance, is such connectivity modulated by recombinatorial demands (Gaesser et al., 2013), novelty of the simulated event (Szpunar et al., 2014), or self-relevance of the retrieval cues (Szpunar et al., 2009; D’Argembeau et al., 2010)? In our experiment, generic word cues were used (e.g., Apple, Car, Newspaper), which may have placed particular demands on hippocampally-mediated retrieval mechanisms. We might expect more vmPFC to hippocampus connectivity if the cues are personally familiar (Benoit et al., 2014) or relate to particular situational schema (Van Kesteren et al., 2012). Finally, we only examined two nodes within the larger core network and the possibility remains that the inclusion of additional nodes (including the right hippocampus) would have changed the overall connectivity pattern including the strength of these connections. Future work should aim to incorporate more regions to determine how information flows within the rest of the system and how it might change at different points during episodic future simulation (e.g., St Jacques et al., 2011; McCormick et al., 2015).

Acknowledgments

This research was supported by National Institute of Mental Health Grant MH060941 to D. L. Schacter and a Tier 2 Canada Research Chair to K. L. Campbell.

Footnotes

1

However, it should be noted that the same winning models are obtained if DCM analyses are performed on the control and specificity induction data separately.

2

However, caution should be exercised when drawing parallels between the animal and human literatures, given pronounced anatomical differences particularly in frontal regions.

References

  1. Addis DR, Wong AT, Schacter DL. Remembering the past and imagining the future: Common and distinct neural substrates during event construction and elaboration. Neuropsychologia. 2007;45:1363–1377. doi: 10.1016/j.neuropsychologia.2006.10.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Andrews-Hanna JR, Reidler JS, Huang C, Buckner RL. Evidence for the default network’s role in spontaneous cognition. J Neurophysiol. 2010a;104:322–335. doi: 10.1152/jn.00830.2009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Andrews-Hanna JR, Reidler JS, Sepulcre J, Poulin R, Buckner RL. Functional-anatomic fractionation of the brain’s default network. Neuron. 2010b;65:550–562. doi: 10.1016/j.neuron.2010.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Benoit RG, Schacter DL. Specifying the core network supporting episodic simulation and episodic memory by activation likelihood estimation. Neuropsychologia. 2015;75:450–457. doi: 10.1016/j.neuropsychologia.2015.06.034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Benoit RG, Szpunar KK, Schacter DL. Ventromedial prefrontal cortex supports affective future simulation by integrating distributed knowledge. Proc Natl Acad Sci U S A. 2014;111:16550–16555. doi: 10.1073/pnas.1419274111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bertossi E, Tesini C, Cappelli A, Ciaramelli E. Ventromedial prefrontal damage causes a pervasive impairment of episodic memory and future thinking. Neuropsychologia. 2015;90:12–24. doi: 10.1016/j.neuropsychologia.2016.01.034. [DOI] [PubMed] [Google Scholar]
  7. Brown TI, Carr VA, LaRocque KF, Favila SE, Gordon AM, Bowles B, Bailenson JN, Wagner AD. Prospective representation of navigational goals in the human hippocampus. Science. 2016;352:1323–1326. doi: 10.1126/science.aaf0784. [DOI] [PubMed] [Google Scholar]
  8. Campbell KL, Grigg O, Saverino C, Churchill N, Grady CL. Age differences in the intrinsic functional connectivity of default network subsystems. Front Aging Neurosci. 2013;5:73. doi: 10.3389/fnagi.2013.00073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. D’Argembeau A, Stawarczyk D, Majerus S, Collette F, Van der Linden M, Feyers D, Maquet P, Salmon E. The neural basis of personal goal processing when envisioning future events. J Cogn Neurosci. 2010;22:1701–13. doi: 10.1162/jocn.2009.21314. [DOI] [PubMed] [Google Scholar]
  10. Demblon J, Bahri MA, D’Argembeau A. Neural correlates of event clusters in past and future thoughts: How the brain integrates specific episodes with autobiographical knowledge. Neuroimage. 2016;127:257–266. doi: 10.1016/j.neuroimage.2015.11.062. [DOI] [PubMed] [Google Scholar]
  11. Friston KJ. Functional and effective connectivity: A review. Brain Connect. 2011;1:13–36. doi: 10.1089/brain.2011.0008. [DOI] [PubMed] [Google Scholar]
  12. Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273–1302. doi: 10.1016/s1053-8119(03)00202-7. [DOI] [PubMed] [Google Scholar]
  13. Gaesser B, Spreng RN, McLelland VC, Addis DR, Schacter DL. Imagining the future: Evidence for a hippocampal contribution to constructive processing. Hippocampus. 2013;23:1150–1161. doi: 10.1002/hipo.22152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Van Kesteren MTR, Ruiter DJ, Fernández G, Henson RN. How schema and novelty augment memory formation. Trends Neurosci. 2012;35:211–219. doi: 10.1016/j.tins.2012.02.001. [DOI] [PubMed] [Google Scholar]
  15. Kurczek J, Wechsler E, Ahuja S, Jensen U, Cohen NJ, Tranel D, Duff M. Differential contributions of hippocampus and medial prefrontal cortex to self-projection and self-referential processing. Neuropsychologia. 2015;73:116–126. doi: 10.1016/j.neuropsychologia.2015.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Madore KP, Szpunar KK, Addis DR, Schacter DL. Episodic specificity induction impacts activity in a core brain network during construction of imagined future experiences. Proc Natl Acad Sci U S A. 2016;113:10696–10701. doi: 10.1073/pnas.1612278113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Martin VC, Schacter DL, Corballis MC, Addis DR. A role for the hippocampus in encoding simulations of future events. Proc Natl Acad Sci U S A. 2011;108:13858–13863. doi: 10.1073/pnas.1105816108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. McCormick C, St-Laurent M, Ty A, Valiante TA, McAndrews MP. Functional and effective hippocampal-neocortical connectivity during construction and elaboration of autobiographical memory retrieval. Cereb Cortex. 2015;25:1297–1305. doi: 10.1093/cercor/bht324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Moscovitch M, Cabeza R, Winocur G, Nadel L. Episodic memory and beyond: The hippocampus and neocortex in transformation. Annu Rev Psychol. 2016;67:105–134. doi: 10.1146/annurev-psych-113011-143733. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Penny WD, Stephan KE, Daunizeau J, Rosa MJ, Friston KJ, Schofield TM, Leff AP. Comparing families of dynamic causal models. PLoS Comput Biol. 2010:6. doi: 10.1371/journal.pcbi.1000709. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Place R, Farovik A, Brockmann M, Eichenbaum H. Bidirectional prefrontal-hippocampal interactions support context-guided memory. Nat Neurosci. 2016;19:992–994. doi: 10.1038/nn.4327. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Preston AR, Eichenbaum H. Interplay of hippocampus and prefrontal cortex in memory. Curr Biol. 2013;23:R764–R773. doi: 10.1016/j.cub.2013.05.041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Ritchey M, Libby LA, Ranganath C. Cortico-hippocampal systems involved in memory and cognition: The PMAT framework. In: O’Mara S, Tsanov M, editors. Progress in Brain Research. Vol. 219. Elsevier; 2015. pp. 45–64. [DOI] [PubMed] [Google Scholar]
  24. Schacter DL, Addis DR, Szpunar KK. Escaping the past: Contributions of the hippocampus to future thinking and imagination. In: Hannula DE, Duff MC, editors. The hippocampus from cells to systems: Structure, connectivity, and functional contributions to memory and flexible cognition. New York: Springer; 2017a. pp. 439–465. [Google Scholar]
  25. Schacter DL, Benoit RG, Szpunar KK. Episodic future thinking: Mechanisms and functions. Curr Opin Behav Sci. 2017b;17:41–50. doi: 10.1016/j.cobeha.2017.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. St Jacques PL, Kragel PA, Rubin DC. Dynamic neural networks supporting memory retrieval. Neuroimage. 2011;57:608–16. doi: 10.1016/j.neuroimage.2011.04.039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Stephan KE, Penny WD, Moran RJ, den Ouden HEM, Daunizeau J, Friston KJ. Ten simple rules for dynamic causal modeling. Neuroimage. 2010;49:3099–3109. doi: 10.1016/j.neuroimage.2009.11.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Szpunar KK, Chan JCK, McDermott KB. Contextual processing in episodic future thought. Cereb Cortex. 2009;19:1539–1548. doi: 10.1093/cercor/bhn191. [DOI] [PubMed] [Google Scholar]
  29. Szpunar KK, Jacques PLS, Robbins CA, Wig GS, Schacter DL. Repetition-related reductions in neural activity reveal component processes of mental simulation. Soc Cogn Affect Neurosci. 2014;9:712–722. doi: 10.1093/scan/nst035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Thakral PP, Benoit RG, Schacter DL. Imagining the future: The core episodic simulation network dissociates as a function of timecourse and the amount of simulated information. Cortex. 2017;90:12–30. doi: 10.1016/j.cortex.2017.02.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Zeithamova D, Dominick AL, Preston AR. Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference. Neuron. 2012;75:168–179. doi: 10.1016/j.neuron.2012.05.010. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES