Mapping Memory Circuits with High-Field FMRI

By Emilie Reas, PLOS Neuroscience Community Editor

How we create and recall memories has long fascinated scientists, spurring decades of research into the brain mechanisms supporting memory. These studies overwhelmingly point to the hippocampus as an essential structure for memory formation; yet despite these efforts, we still don’t fully understand how hippocampal circuits transform stimulus input into stored memories, in part due to several fundamental methodological challenges.

The most commonly used functional imaging method in humans, fMRI, neither measures neural activity directly nor attains ideal spatiotemporal resolutions. Although more powerful, invasive techniques can be used in animals, it’s arguable whether they can be applied to assess higher cognitive functions like episodic memory, as the jury’s out on whether this process is uniquely human or shared with animals. However, recent neuroimaging advances are rapidly narrowing the power gap between invasive and non-invasive techniques, helping to reconcile findings across animal and human studies. In particular, high-field, high-resolution fMRI in humans is becoming more feasible, permitting sub-millimeter spatial resolution. Although the BOLD signal from fMRI only approximates the neural signal, such methodological advances get us one step closer to imaging neural activity during cognitive functions like memory formation. A team of researchers recently took advantage of high-field fMRI to investigate sub-region and layer-specific memory activity in the medial temporal lobe, an area critical for long-term memory acquisition.

The hippocampal-entorhinal circuit

Within the medial temporal lobe, the entorhinal cortex (EC) and hippocampus (including subfields dentate gyrus, CA1, CA2 and CA3) make up a well-characterized circuit, in which superficial EC layers project to the dentate gyrus and CA1 via the perforant path, on to CA3 via mossy fibers, to CA1 via schaffer collaterals, and finally return back to the deep layers of the EC. We know this circuit is important for memory, as the hippocampus is essential for memory encoding and other processes that presumably support memory, including novelty detection or pattern separation and completion. However, the mapping of these functions onto human entorhinal-hippocampal pathways is incomplete. 

Imaging memory with high-field fMRI

To examine how novelty and memory signals are distributed along the EC-hippocampus circuit, Maass and colleagues conducted high-resolution (0.8 mm isotropic voxels) 7T fMRI while participants performed an incidental encoding task. The subjects viewed a series of novel and familiar scenes during scanning, and later completed a surprise memory recall test on the scenes they had previously seen. This allowed the researchers to assess brain activity related to novelty – by comparing novel and familiar trials – as well as activity related to successful memory encoding – by comparing trials that were subsequently remembered and forgotten. On each subject’s structural brain image, they parcellated the EC into superficial input layers and deep output layers, and segmented the hippocampus into CA1 and a combined dentate gyrus/CA2/CA3 region (DG/CA2/3). 

Double-dissociation of novelty and encoding signals

Across participants, novel scenes activated DG/CA2/3, whereas successful encoding activated CA1, and the strength of this CA1 signal predicted retrieval accuracy. Next, Maass and colleagues looked at subject-level voxel-wise activity, which preserves high spatial resolution by eliminating the need for smoothing and across-subject averaging. Using multivariate Bayes decoding, which can be used to compare the log evidence that various regions predict a particular cognitive state, they evaluated whether EC or hippocampal regions predict novelty and memory encoding. As illustrated by the relative log evidences in the below graphs, DG/CA2/3 (A right) and CA1 (B right) respectively signaled novelty and encoding, consistent with their group-level findings. But this analysis further showed that superficial EC (the input layers to the hippocampus) and deep EC (the output layers from the hippocampus) also respectively predicted novelty (A left) and encoding (B left). What’s more, superficial EC and DG/CA2/3 functionally coupled during novelty processing, whereas deep EC and CA1 coupled during encoding. 

In essence, these findings suggest a division of labor across the EC-hippocampal circuit, where hippocampal input pathways participate in novelty detection, and output pathways transform these signals for memory storage. The researchers offer a model in which information about stimulus identity feeds in from upstream regions such as the perirhinal and parahippocampal cortices, which are known to process object and scene identity. Hippocampal pattern separation or comparator computations might then be performed to both assess novelty and reduce interference between stimulus representations, transforming the novelty signal into output for long-term storage. This explanation for how hippocampal circuits process a stimulus representation is reasonable, considering that DG/CA3 is important for pattern separation, and CA1 has been proposed as a neural comparator, processes which may determine the memory fate of a stimulus representation.

Cautions and caveats

A segregation of function across EC layers and hippocampal subfields does not necessarily imply that these mappings are mutually exclusive. For instance, it’s likely that output pathways still carry a novelty signal, and memory formation may begin earlier in the processing stream than detected here. Despite the impressive resolution in this study, allowing fine segmentation of cortical layers and subregions, noise and artifact are inherent concerns for any fMRI study. As the BOLD signal is a crude estimate of neural activity, there may well be a ceiling to the power of high-field fMRI, even with the most rigorous methods. How accurately these region- and layer-specific signals map onto memory functions therefore remains to be validated. And of course, we can’t infer directionality, causality or any direct relationship to neural activity from fMRI alone. It’s tempting to interpret early circuit activity as an input signal and late activity as an output signal, or to assume that the BOLD response reflects excitatory neural activity; however, we’ll need more direct neuroimaging tools to trace the flow of neural signal and confirm these speculations.

Together, Maass and colleagues’ study advances the field of cognitive neuroscience on two fronts. First, it helps bridge the gap between robust yet invasive imaging tools and non-invasive but less powerful approaches commonplace in human imaging studies. Their successful application of high-field fMRI demonstrates the feasibility of assessing human brain activity with sub-millimeter resolution, paving the way for the standardization and refinement of these tools. Second, and perhaps most critically, it allows us to peer into the brain at previously impossible scales to view the live hippocampal circuit hard at work, processing and engendering memories. While past fMRI studies have effectively shown where memories are woven together, these findings refine this anatomical precision to bring us one step closer to understanding how hippocampal circuits accomplish this feat.

First author Anne Maass kindly offered to answer a few questions about her research. Here is a brief interview with Maass and her colleagues.

Are there unique methodological concerns to consider when using high-field, high-resolution fMRI?

The increased signal-to-noise ratio provided by MRI at 7T enables us to acquire fMRI data at an unprecedented level of anatomical detail. However, ultra high-field fMRI is also more vulnerable to distortions and susceptibility-related artifacts and the negative effect of motion increases with resolution.

In particular, the anterior medial temporal lobe regions, such as the entorhinal cortex and perirhinal cortex, are often affected by susceptibility artifacts. Nevertheless, an optimized 7T protocol as we used in our study can reduce (but not fully eliminate) these signal dropouts and distortions, e.g. by the very small voxel size, shorter echo times as well as optimized shimming and distortion correction. We therefore had to manually discard functional volumes with visible dropouts and distortions.

The analysis of high-resolution functional data raises additional challenges, for instance the precise coregistration of structural and functional (often partial) images or the normalization into a standard space, which is usually done for group comparisons. In our study, we aimed to evaluate functional differences between entorhinal and hippocampal layers and subregions. We thus manually defined our regions of interest and chose a novel approach that enables to use the individual (raw) functional data to achieve highest anatomical precision.

Have other studies examined the hippocampal-entorhinal circuit during memory encoding or novelty detection using more direct neural imaging tools, for example, with intracranial EEG? If so, how do they align with your findings?

Although there have been several intracranial EEG recording studies in humans that investigated functional coupling between hippocampus and EC (i.e. Fernandez et al., 1999), to our knowledge, these studies have not been able to look at deep versus superficial EC or at specific hippocampal subfields.

Your findings have obvious implications for memory disorders. Have you done any work investigating how the hippocampal-entorhinal memory circuit is disrupted in Alzheimer’s or other dementias?

To investigate layer-specific processing in aging or neurodegenerative diseases is of course of particular interest as aging seems to affect particularly entorhinal input from superficial EC layers to the dentate gyrus and also taupathology in Alzheimer’s disease emerges in the superficial EC layers, subsequently spreading to particular hippocampal subregions or layers (i.e. CA1 apical layers). However, high-resolution fMRI at 7T is particularly challenging in older people. The high probability of exclusion criteria (e.g. implants) complicates subject recruitment and stronger subject movement increases motion artefacts. So far we have collected functional data at 7T in healthy older people with 1mm isotropic resolution that we are currently analyzing. In addition, further studies are planned that focus on changes in intrinsic functional connectivity of the hippocampal-entorhinal network in early Alzheimer’s disease.

What further questions do your results raise regarding hippocampal memory pathways, and do you have plans to follow-up on these questions with future studies?

One further question that we are currently addressing is how hippocampal and neocortical connectivity with the EC is functionally organized in humans.

While the rodent EC shows a functional division into lateral and medial parts based on differential anatomical connectivity with parahippocampal and hippocampal subregions, almost nothing is known about functional subdivisions of the human EC.

In addition to characterizing entorhinal functional connectivity profiles in young adults, we also want to study how these are altered by exercise training. Finally, we aim to resolve how aging affects object vs. scene processing (and pattern separation) in different components of the EC and subfields of the hippocampus.

Any views expressed are those of the author, and do not necessarily reflect those of PLOS.

References

Dere E et al. (2006). The case for episodic memory in animals. Neurosci Biobehav Rev 30(8):1206-24. doi:10.1016/j.neubiorev.2006.09.005

Fernandez G et al. (1999). Real-Time Tracking of Memory Formation in the Human Rhinal Cortex and Hippocampus. Science 285(5433):1582-5. doi:1 0.1126/science.285.5433.1582

Friston K et al. (2008). Bayesian decoding of brain images. Neuroimage 39(1):181-205. doi:10.1016/j.neuroimage.2007.08.013

Maass A, et al. (2014). Laminar activity in the hippocampus and entorhinal cortex related to novelty and episodic encoding. Nat Commun 5:5547. doi:10.1038/ncomms6547

Emilie Reas received her PhD in Neuroscience from UC San Diego, where she used fMRI to study memory. As a postdoc at UCSD, she currently studies how the brain changes with aging and disease. In addition to her tweets for @PLOSNeuro she is @etreas.