Our lab focuses on the cognitive and neural mechanisms of episodic (EM) in young and older adults. At present, we are pursuing five main lines of research, which we investigate in relation to basic neurocognitive mechanisms and in relation to aging. This table shows representative publications and each of the five lines of research is described below.

1. EM representations

Traditional fMRI studies focused on EM processes but it is now possible to use fMRI to examine also the quality of EM representations, using methods such as representational similarity analysis (RSA). Using RSA, we developed a new approach to measure encoding-retrieval similarity (ERS), which revealed scene-specific reactivation during scene recognition (Ritchey et al., 2013) and recall (Wing et al., 2015, see Fig. A). We also use RSA to examine the neural mechanisms of false memories (Wing et al., under review) and to disentangle sensory vs. semantic features of EM representations (Davis et al, in prep. see Fig. B). To further analyze the features of EM representations, we combine RSA with convolutional deep neural networks (DNN) models. For example, in one study we found that aging impaired sensory features in early visual cortex but enhanced semantic features in anterior temporal lobes (ATL) and medial PFC (Monge et al., under review, Fig. C). We are currently combining representations analyses with experimental manipulations affecting different features of memory representations.

  • Ritchey M, Wing EA, LaBar KS, Cabeza R (2013) Neural Similarity Between Encoding and Retrieval is Related to Memory Via Hippocampal Interactions. Cerebral cortex 23:2818-2828.
  • Wing EA, Ritchey M, Cabeza R (2015) Reinstatement of Individual Past Events Revealed by the Similarity of Distributed Activation Patterns during Encoding and Retrieval. J Cogn Neurosci 27:679-691.
  • Davis SW, Wang WC, Wing EA, Geib BR, Cabeza R (in preparation) Contribution of visual and semantic objects representations to subsequent perceptual and conceptual memory.
  • Monge ZA, Wing EA, Geib BR, Davis SW, Cabeza R (under review) Age-related dedifferentiation and hyperdifferentiation of perceptual and mnemonic representations.

2. EM networks

EM representations are processed by a distributed network of brain regions, which we investigate using functional and structural connectivity and structural connectivity. We analyze functional connectivity (co-variance in neural activity) using graph theory. Our graph theory studies indicate that successful EM depends on network integration, particularly involving medial temporal lobe regions, such as the hippocampus. For example, we found that as a function of retrieval success, the hippocampus was the brain region showing the greatest increase in integration (Geib et al., 2017, Cereb Ctx, Fig. A), particularly with subset of regions we called “retrieval assembly” (Geib et al., 2017, HBM, Fig. B). Regarding aging, we found greater medial temporal lobe integration as a function of successful retrieval in older than younger adults, suggesting functional compensation (Monge et al, 2017, Neurobio Aging, Fig. C). However, functional connectivity in older adults is constrained by structural (white-matter) connectivity, which we measure at the level of individual white-matter tracts using diffusion tensor imaging (DTI) tractography (e.g., Davis et al., in press, Network Neurosci, Fig. D).

  • Geib BR, Stanley ML, Wing EA, Laurienti PJ, Cabeza R (2017) Hippocampal Contributions to the Large-Scale EM Network Predict Vivid Visual Memories. Cerebral cortex 27:680-693.
  • Geib BR, Stanley ML, Dennis NA, Woldorff MG, Cabeza R (2017) From Hippocampus to Whole-Brain: The Role of Integrative Processing in EM Retrieval. Hum Brain Mapp 38:2242-2259.
  • Monge ZA, Stanley ML, Geib BR, Davis SW, Cabeza R (2018) Functional networks underlying item and source memory: shared and distinct network components and age-related differences. Neurobiol Aging 69:140-150.
  • Davis SW, Szymanski A, Boms H, Fink T, Cabeza R (in press) Cooperative contributions of structural and functional connectivity to successful memory in aging. Network Neuroscience.

3. Modulating EM with transcranial magnetic stimulation (TMS)

A limitation of fMRI is that it can only establish correlational links. To address this limitation, we complement fMRI with TMS, which can establish causal links. In one study (Davis et al, 2017, HBM), we found brain activity predicting subsequent memory was enhanced by excitatory (5Hz) repetitive TMS (rTMS) but attenuated by inhibitory (1Hz) rTMS (Fig. A). rTMS effects were not limited to the stimulated region but spread across the brain, particularly for inhibitory rTMS. Inhibitory (1Hz) rTMS boosted functional connectivity with distant regions, including contralateral brain regions, consistent with compensation (Fig. B). In a follow-up representational similarity analysis (RSA, Wang et al., in press, Cog Neurosci), we found that excitatory rTMS boosted the quality of memory representation in the hippocampus (encoding-retrieval similarity—ERS). Turning to aging, we are currently using rTMS to enhance memory in older adults. We have found (Beynel et al, under review) that excitatory rTMS can boost memory performance in difficult working memory trials. Finally, we have a collaboration to investigate the neural mechanisms of rTMS, which combines fMRI and EEG measures in humans with single-cell recording in monkeys.

  • Davis SW, Luber B, Murphy DL, Lisanby SH, Cabeza R (2017) Frequency-specific neuromodulation of local and distant connectivity in aging & EM function. Hum Brain Mapp 38:5987–6004.
  • Wang WC, Wing EA, Murphy DLK, Cabeza R, Davis SW (in press) Excitatory TMS Boosts Memory Representations. Cogn Neurosci.
  • Beynel L, Davis SW, Crowell CC, Hilbig SA, Lim W, Palmer H, Brito A, Peterchev AV, Luber B, Lisanby SH, Cabeza R, Appelbaum LG (in review) Online repetitive transcranial magnetic stimulation during working memory in younger and older adults.

4. EM in the real world: autobiographical memory

Autobiographical memory (AM), or memory for events from your own life, is a combination of EM with knowledge about your life structure (lifetime periods, repeated events, routines, etc.). Most neuroscience studies of EM have focused on memory for “miniature events” produced in the laboratory (a word presented on a computer screen). Although AM and laboratory memory engage similar brain regions (Cabeza et al., 2004) there are also substantial differences (for a review, see Cabeza & St. Jacques, 2007). In one line of AM research in our lab, participants tank photos of AM events, which we then use to elicit AMs (Cabeza et al., 2004; St. Jacques et al., 2008). We currently use lifelogging cameras that continuously record real-life events from a first-person perspective without disrupting the natural experience (Fig. A). Compared to the verbal cues typically used to elicit AMs, lifelogging photos elicit greater hippocampal activity (Fig. B) and functional connectivity in the associated default mode network (in yellow in Fig. C, St. Jacques et al., 2011, JoCN). We are currently using lifelogging photos to measure and train AMs in older adults.

  • Cabeza R, Prince SE, Daselaar SM, Greenberg D, Budde M, Dolcos F, LaBar KS, Rubin DC (2004) Comparing the neural correlates of autobiographical and EM with a new fMRI paradigm. J Cog Neurosci 9:1583-1594.
  • Cabeza R, St Jacques P (2007) Functional neuroimaging of autobiographical memory. Trends Cogn Sci 11:219-227.
  • St Jacques PL, Rubin DC, Labar KS, Cabeza R (2008) The Short and Long of It: Neural Correlates of Temporal-order Memory for Autobiographical Events. J Cogn Neurosci 20:1327-1341.
  • St Jacques PL, Conway MA, Lowder MW, Cabeza R (2011) Watching my mind unfold versus yours: an fMRI study using a novel camera technology to examine neural differences in self-projection of self versus other perspectives. J Cogn Neurosci 23:1275-1284.

5. Interactions of EM with other cognitive functions

The neural mechanisms EM largely overlap the mechanisms of other cognitive abilities. We investigate similarity, differences, and interactions between EM and other functions, such as such as attention, semantic memory, and decision making. For example, we found that for both EM retrieval and visual attention, dorsal parietal regions mediate top-down attention processes and ventral parietal regions, bottom-up attention processes (Fig. A, Cabeza et al., 2011, JoCN); a finding consistent with our Attention to Memory (AtoM) model (Cabeza et al., 2008, Nature Rev Neurosci). We are also interested in the interactions between EM and semantic memory (SM). For example, repeated statements are judged as more likely to be true, a phenomenon known as “illusory truth.” We have linked illusory truth to a medial temporal lobe region associated with processing fluency (Dew et al., 2011), perirhinal cortex, where “true” ratings elicited greater activity for repeated than novel statements (Fig. B, Wang et al., 2016). We are also examining the interactions between EM and decision making (DM), particularly in relation to aging. Aging impairs both EM and DM, making memory-based DM particularly challenging for older adults. Yet, older adults may partly compensate for deficits in memory-based DM by over-recruiting medial prefrontal cortex (Fig. C, Lighthall et al., 2014, J Neurosci, mPFC activity was associated with faster reaction times in older adults). We have also investigated memory and DM interactions in older adults using a reinforcement learning paradigm (Lighthall et al., 2018, J Neurosci). Currently, we are investigating the effects of aging on memory-based DM using multi-attribute DM tasks and delayed discounting tasks.

  • Cabeza R, Mazuz YS, Stokes J, Kragel JE, Woldorff MG, Ciaramelli E, Olson IR, Moscovitch M (2011) Overlapping parietal activity in memory and perception: evidence for the attention to memory model. J Cogn Neurosci 23:3209-3217.
  • Cabeza R, Ciaramelli E, Olson IR, Moscovitch M (2008) The parietal cortex and episodic memory: an attentional account. Nat Rev Neurosci 9:613-625.
  • Dew IT, Cabeza R (2013) A broader view of perirhinal function: from recognition memory to fluency-based decisions. J Neurosci 33:14466-14474.
  • Wang W-C, Brashier NM, Wing EA, Marsh EJ, Cabeza R (2016) On known unknowns: Fluency and the neural mechanisms of illusory truth. J Cogn Neurosci 28:739-746.
  • Lighthall NR, Huettel SA, Cabeza R (2014) Functional compensation in the ventromedial prefrontal cortex improves memory-dependent decisions in older adults. J Neurosci, 34:15648-15657.
  • Lighthall NR, Pearson JM, Huettel SA, Cabeza R (2018) Feedback-based learning in aging: Contributions and trajectories of change in striatal and hippocampal systems. J Neurosci:0769-0718.