Content representation in the human medial temporal lobe

The transformation of sensory inputs into complex memory representations is fundamental to human experience; yet, little is known about how this crucial process is achieved. When you meet your friend at the new cafe in town, what part of the brain encodes this novel scene into long term memory? What part encoded your friend’s favorite t-shirt, so that the sight of it gives you a feeling of familiarity rather than surprise? It is well-established that the medial temporal lobe (MTL) is crucial to both processes, but the MTL is not a single homogeneous region. In fact, it is composed of several anatomically distinct subregions including hippocampus, perirhinal cortex (PRC) and parahippocampal cortex (PHC). However, the computations performed by each subregion to encode individual events is still unclear. The present research tests the central hypothesis that different forms of event content are transformed into memory by distinct subregions within the MTL. A critical barrier in the study of content representation thus far has been its focus on comparing univariate peak activations in a region to different stimulus materials. To go beyond this limited approach, we employed multivariate statistical analyses that takes into account how event content is represented by distributed activity in MTL subregions. First, we examine the content-specific contributions of MTL subregions to episodic encoding and retrieval. Then, we demonstrate how these distributed representations support memory-based prediction to resolve ambiguities in our environment.Neuroscienc

Interactions between the hippocampus and prefrontal cortex in context-dependent overlapping memory retrieval

Activation in the hippocampus (HC) and prefrontal cortex (PFC) is critical to accurately retrieve overlapping sequences. Experiments 1 and 2 tested the hypotheses that activation in and interaction between HC and PFC increases as overlap between sequences increases in a non-spatial task. Experiment 3 tested the hypothesis that theta oscillations are involved in orchestrating interactions between HC and PFC in a spatial task with overlapping elements. In the first two studies, 17 participants (aged 18-34; 11 female) learned sequences consisting of a picture frame, face, and scene. Conditions varied by degree of overlap. Using fMRI, Experiment 1 tested how degree of overlap affected HC and PFC activation. In overlapping sequences, middle and posterior HC were active when predictability of the correct response increased, dorsolateral PFC was active when participants were able to ascertain the correct set of sequences, and ventrolateral PFC was active when inhibition of interfering associations was required. Experiment 2 examined functional connectivity of HC and PFC during disambiguation. Low- and high-overlap conditions were associated with increased connectivity in separate regions at different times indicating that retrieval under the two conditions used different neural networks and strategies. Low-overlap trials were associated with increased connectivity between HC and prefrontal and parietal regions. High-overlap trials showed increased connectivity between lateral PFC and visual areas, indicating that imagery may be necessary for accurate performance. Using EEG recording, Experiment 3 examined theta activity during retrieval of well-learned, overlapping and non-overlapping mazes in 17 participants (aged 18-34, 11 female). Theta activity increased in overlapping mazes during the first of four hallways, suggesting participants were looking ahead to upcoming turns in the maze. Theta activity increased at the beginning and choice point of the third overlapping hallway, possibly in response to interference from the paired, overlapping maze. These studies provide evidence that (1) overlapping associations in non-spatial sequences elicit interactions between hippocampus and lateral prefrontal cortex, (2) increasing the degree of overlap changes the neural processes required to perform the task, and (3) theta power increases in response to increased cognitive demand and maintenance of sequence information needed to differentiate between overlapping spatial routes

Which Way Was I Going? Contextual Retrieval Supports the Disambiguation of Well Learned Overlapping Navigational Routes

Groundbreaking research in animals has demonstrated that the hippocampus contains neurons that distinguish betweenoverlapping navigational trajectories. These hippocampal neurons respond selectively to the context of specific episodes despite interference from overlapping memory representations. The present study used functional magnetic resonanceimaging in humans to examine the role of the hippocampus and related structures when participants need to retrievecontextual information to navigate well learned spatial sequences that share common elements. Participants were trained outside the scanner to navigate through 12 virtual mazes from a ground-level first-person perspective. Six of the 12 mazes shared overlapping components. Overlapping mazes began and ended at distinct locations, but converged in the middle to share some hallways with another maze. Non-overlapping mazes did not share any hallways with any other maze. Successful navigation through the overlapping hallways required the retrieval of contextual information relevant to thecurrent navigational episode. Results revealed greater activation during the successful navigation of the overlapping mazes compared with the non-overlapping mazes in regions typically associated with spatial and episodic memory, including thehippocampus, parahippocampal cortex, and orbitofrontal cortex. When combined with previous research, the current findings suggest that an anatomically integrated system including the hippocampus, parahippocampal cortex, and orbitofrontal cortexis critical for the contextually dependent retrieval of well learned overlapping navigational routes

Context-specific activation of hippocampus and SN/VTA by reward is related to enhanced long-term memory for embedded objects

Animal studies indicate that hippocampal representations of environmental context modulate reward-related processing in the substantia nigra and ventral tegmental area (SN/VTA), a major origin of dopamine in the brain. Using functional magnetic resonance imaging (fMRI) in humans, we investigated the neural specificity of context-reward associations under conditions where the presence of perceptually similar neutral contexts imposed high demands on a putative hippocampal function, pattern separation. The design also allowed us to investigate how contextual reward enhances long-term memory for embedded neutral objects. SN/VTA activity underpinned specific context-reward associations in the face of perceptual similarity. A reward-related enhancement of long-term memory was restricted to the condition where the rewarding and the neutral contexts were perceptually similar, and in turn was linked to co-activation of the hippocampus (subfield DG/CA3) and SN/VTA. Thus, an ability of contextual reward to enhance memory for focal objects is closely linked to context-related engagement of hippocampal-SN/VTA circuitry

Predictability matters: role of the hippocampus and prefrontal cortex in disambiguation of overlapping sequences

Previous research has demonstrated that areas in the medial temporal lobe and prefrontal cortex (PFC) show increased activation during retrieval of overlapping sequences. In this study, we designed a task in which degree of overlap varied between conditions in order to parse out the contributions of hippocampal and prefrontal subregions as overlap between associations increased. In the task, participants learned sequential associations consisting of a picture frame, a face within the picture frame, and an outdoor scene. The control condition consisted of a single frame-face-scene sequence. In the low overlap condition, each frame was paired with two faces and two scenes. In the high overlap condition, each frame was paired with four faces and four scenes. In all conditions the correct scene was chosen among four possible scenes and was dependent on the frame and face that preceded the choice point. One day after training, participants were tested on the retrieval of learned sequences during fMRI scanning. Results showed that the middle and posterior hippocampus (HC) was active at times when participants acquired information that increased predictability of the correct response in the overlapping sequences. Activation of dorsolateral PFC occurred at time points when the participant was able to ascertain which set of sequences the correct response belonged to. The ventrolateral PFC was active when inhibition was required, either of irrelevant stimuli or incorrect responses. These results indicate that areas of lateral PFC work in concert with the HC to disambiguate between overlapping sequences and that sequence predictability is key to when specific brain regions become active

Heritability and reliability of automatically segmented human hippocampal formation subregions

The human hippocampal formation can be divided into a set of cytoarchitecturally and functionally distinct subregions, involved in different aspects of memory formation. Neuroanatomical disruptions within these subregions are associated with several debilitating brain disorders including Alzheimer's disease, major depression, schizophrenia, and bipolar disorder. Multi-center brain imaging consortia, such as the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) consortium, are interested in studying disease effects on these subregions, and in the genetic factors that affect them. For large-scale studies, automated extraction and subsequent genomic association studies of these hippocampal subregion measures may provide additional insight. Here, we evaluated the test-retest reliability and transplatform reliability (1.5 T versus 3 T) of the subregion segmentation module in the FreeSurfer software package using three independent cohorts of healthy adults, one young (Queensland Twins Imaging Study, N=39), another elderly (Alzheimer's Disease Neuroimaging Initiative, ADNI-2, N=163) and another mixed cohort of healthy and depressed participants (Max Planck Institute, MPIP, N=598). We also investigated agreement between the most recent version of this algorithm (v6.0) and an older version (v5.3), again using the ADNI-2 and MPIP cohorts in addition to a sample from the Netherlands Study for Depression and Anxiety (NESDA) (N=221). Finally, we estimated the heritability (h(2)) of the segmented subregion volumes using the full sample of young, healthy QTIM twins (N=728). Test-retest reliability was high for all twelve subregions in the 3 T ADNI-2 sample (intraclass correlation coefficient (ICC)=0.70-0.97) and moderate-to-high in the 4 TQTIM sample (ICC=0.5-0.89). Transplatform reliability was strong for eleven of the twelve subregions (ICC=0.66-0.96); however, the hippocampal fissure was not consistently reconstructed across 1.5 T and 3 T field strengths (ICC=0.47-0.57). Between-version agreement was moderate for the hippocampal tail, subiculum and presubiculum (ICC=0.78-0.84; Dice Similarity Coefficient (DSC)=0.55-0.70), and poor for all other subregions (ICC=0.34-0.81; DSC=0.28-0.51). All hippocampal subregion volumes were highly heritable (h(2)=0.67-0.91). Our findings indicate that eleven of the twelve human hippocampal subregions segmented using FreeSurfer version 6.0 may serve as reliable and informative quantitative phenotypes for future multi-site imaging genetics initiatives such as those of the ENIGMA consortium. (C) 2016 The Authors. Published by Elsevier Inc

Functional MRI investigations of overlapping spatial memories and flexible decision-making in humans

Thesis (Ph.D.)--Boston UniversityResearch in rodents and computational modeling work suggest a critical role for the hippocampus in representing overlapping memories. This thesis tested predictions that the hippocampus is important in humans for remembering overlapping spatial events, and that flexible navigation of spatial routes is supported by key prefrontal and striatal structures operating in conjunction with the hippocampus. The three experiments described in this dissertation used functional magnetic resonance imaging (fMRI) in healthy young people to examine brain activity during context-dependent navigation of virtual maze environments. Experiment 1 tested whether humans recruit the hippocampus and orbitofrontal cortex to successfully retrieve well-learned overlapping spatial routes. Participants navigated familiar virtual maze environments during fMRI scanning. Brain activity for flexible retrieval of overlapping spatial memories was contrasted with activity for retrieval of distinct non-overlapping memories. Results demonstrate the hippocampus is more strongly recruited for planning and retrieval of overlapping routes than non-overlapping routes, and the orbitofrontal cortex is recruited specifically for context-dependent navigational decisions. Experiment 2 examined whether the hippocampus, orbitofrontal cortex, and striatum interact cooperatively to support flexible navigation of overlapping routes. Using a functional connectivity analysis of fMRI data, we compared interactions between these structures during virtual navigation of overlapping and non-overlapping mazes. Results demonstrate the hippocampus interacts with the caudate more strongly for navigating overlapping than non-overlapping routes. Both structures cooperate with the orbitofrontal cortex specifically during context-dependent decision points, suggesting the orbitofrontal cortex mediates translation of contextual information into the flexible selection of behavior. Experiment 3 examined whether the hippocampus and caudate contribute to forming context-dependent memories. fMRI activity for learning new virtual mazes which overlap with familiar routes was compared with activity for learning completely distinct routes. Results demonstrate both the hippocampus and caudate are preferentially recruited for learning mazes which overlap with existing route memories. Furthermore, both areas update their responses to familiar route memories which become context-dependent, suggesting complementary roles in both learning and updating overlapping representations. Together, these studies demonstrate that navigational decisions based on overlapping representations rely on a network incorporating hippocampal function with the evaluation and selection of behavior in the prefrontal cortex and striatum

Memory capacity in the hippocampus

Neural assemblies in hippocampus encode positions. During rest, the hippocam- pus replays sequences of neural activity seen during awake behavior. This replay is linked to memory consolidation and mental exploration of the environment. Re- current networks can be used to model the replay of sequential activity. Multiple sequences can be stored in the synaptic connections. To achieve a high mem- ory capacity, recurrent networks require a pattern separation mechanism. Such a mechanism is global remapping, observed in place cell populations. A place cell fires at a particular position of an environment and is silent elsewhere. Multiple place cells usually cover an environment with their firing fields. Small changes in the environment or context of a behavioral task can cause global remapping, i.e. profound changes in place cell firing fields. Global remapping causes some cells to cease firing, other silent cells to gain a place field, and other place cells to move their firing field and change their peak firing rate. The effect is strong enough to make global remapping a viable pattern separation mechanism. We model two mechanisms that improve the memory capacity of recurrent net- works. The effect of inhibition on replay in a recurrent network is modeled using binary neurons and binary synapses. A mean field approximation is used to de- termine the optimal parameters for the inhibitory neuron population. Numerical simulations of the full model were carried out to verify the predictions of the mean field model. A second model analyzes a hypothesized global remapping mecha- nism, in which grid cell firing is used as feed forward input to place cells. Grid cells have multiple firing fields in the same environment, arranged in a hexagonal grid. Grid cells can be used in a model as feed forward inputs to place cells to produce place fields. In these grid-to-place cell models, shifts in the grid cell firing patterns cause remapping in the place cell population. We analyze the capacity of such a system to create sets of separated patterns, i.e. how many different spatial codes can be generated. The limiting factor are the synapses connecting grid cells to place cells. To assess their capacity, we produce different place codes in place and grid cell populations, by shuffling place field positions and shifting grid fields of grid cells. Then we use Hebbian learning to increase the synaptic weights be- tween grid and place cells for each set of grid and place code. The capacity limit is reached when synaptic interference makes it impossible to produce a place code with sufficient spatial acuity from grid cell firing. Additionally, it is desired to also maintain the place fields compact, or sparse if seen from a coding standpoint. Of course, as more environments are stored, the sparseness is lost. Interestingly, place cells lose the sparseness of their firing fields much earlier than their spatial acuity. For the sequence replay model we are able to increase capacity in a simulated recurrent network by including an inhibitory population. We show that even in this more complicated case, capacity is improved. We observe oscillations in the average activity of both excitatory and inhibitory neuron populations. The oscillations get stronger at the capacity limit. In addition, at the capacity limit, rather than observing a sudden failure of replay, we find sequences are replayed transiently for a couple of time steps before failing. Analyzing the remapping model, we find that, as we store more spatial codes in the synapses, first the sparseness of place fields is lost. Only later do we observe a decay in spatial acuity of the code. We found two ways to maintain sparse place fields while achieving a high capacity: inhibition between place cells, and partitioning the place cell population so that learning affects only a small fraction of them in each environment. We present scaling predictions that suggest that hundreds of thousands of spatial codes can be produced by this pattern separation mechanism. The effect inhibition has on the replay model is two-fold. Capacity is increased, and the graceful transition from full replay to failure allows for higher capacities when using short sequences. Additional mechanisms not explored in this model could be at work to concatenate these short sequences, or could perform more complex operations on them. The interplay of excitatory and inhibitory populations gives rise to oscillations, which are strongest at the capacity limit. The oscillation draws a picture of how a memory mechanism can cause hippocampal oscillations as observed in experiments. In the remapping model we showed that sparseness of place cell firing is constraining the capacity of this pattern separation mechanism. Grid codes outperform place codes regarding spatial acuity, as shown in Mathis et al. (2012). Our model shows that the grid-to-place transformation is not harnessing the full spatial information from the grid code in order to maintain sparse place fields. This suggests that the two codes are independent, and communication between the areas might be mostly for synchronization. High spatial acuity seems to be a specialization of the grid code, while the place code is more suitable for memory tasks. In a detailed model of hippocampal replay we show that feedback inhibition can increase the number of sequences that can be replayed. The effect of inhibition on capacity is determined using a meanfield model, and the results are verified with numerical simulations of the full network. Transient replay is found at the capacity limit, accompanied by oscillations that resemble sharp wave ripples in hippocampus. In a second model Hippocampal replay of neuronal activity is linked to memory consolidation and mental exploration. Furthermore, replay is a potential neural correlate of episodic memory. To model hippocampal sequence replay, recurrent neural networks are used. Memory capacity of such networks is of great interest to determine their biological feasibility. And additionally, any mechanism that improves capacity has explanatory power. We investigate two such mechanisms. The first mechanism to improve capacity is global, unspecific feedback inhibition for the recurrent network. In a simplified meanfield model we show that capacity is indeed improved. The second mechanism that increases memory capacity is pattern separation. In the spatial context of hippocampal place cell firing, global remapping is one way to achieve pattern separation. Changes in the environment or context of a task cause global remapping. During global remapping, place cell firing changes in unpredictable ways: cells shift their place fields, or fully cease firing, and formerly silent cells acquire place fields. Global remapping can be triggered by subtle changes in grid cells that give feed-forward inputs to hippocampal place cells. We investigate the capacity of the underlying synaptic connections, defined as the number of different environments that can be represented at a given spatial acuity. We find two essential conditions to achieve a high capacity and sparse place fields: inhibition between place cells, and partitioning the place cell population so that learning affects only a small fraction of them in each environments. We also find that sparsity of place fields is the constraining factor of the model rather than spatial acuity. Since the hippocampal place code is sparse, we conclude that the hippocampus does not fully harness the spatial information available in the grid code. The two codes of space might thus serve different purposes

Dual coding with STDP in a spiking recurrent neural network model of the hippocampus.

The firing rate of single neurons in the mammalian hippocampus has been demonstrated to encode for a range of spatial and non-spatial stimuli. It has also been demonstrated that phase of firing, with respect to the theta oscillation that dominates the hippocampal EEG during stereotype learning behaviour, correlates with an animal's spatial location. These findings have led to the hypothesis that the hippocampus operates using a dual (rate and temporal) coding system. To investigate the phenomenon of dual coding in the hippocampus, we examine a spiking recurrent network model with theta coded neural dynamics and an STDP rule that mediates rate-coded Hebbian learning when pre- and post-synaptic firing is stochastic. We demonstrate that this plasticity rule can generate both symmetric and asymmetric connections between neurons that fire at concurrent or successive theta phase, respectively, and subsequently produce both pattern completion and sequence prediction from partial cues. This unifies previously disparate auto- and hetero-associative network models of hippocampal function and provides them with a firmer basis in modern neurobiology. Furthermore, the encoding and reactivation of activity in mutually exciting Hebbian cell assemblies demonstrated here is believed to represent a fundamental mechanism of cognitive processing in the brain

Neural Encoding of Local vs. Global Space: From Structure to Function

The retrosplenial cortex may be important for navigating visually similar compartmentalised spaces by conjunctively encoding both local and global environments. Previously, a novel directional signal that encodes local spaces was found in the dysgranular retrosplenial cortex (dRSC) while global head direction encoding was found in both dRSC and granular retrosplenial cortex (gRSC; Jacob et al., 2017). This thesis addresses two questions arising from this finding: (i) how does the local directional signal arise? and (ii) do the downstream place cells (cells that display spatially constrained firing) display local or global encoding? The first question was explored by retrogradely labelling the neuronal inputs into the two retrosplenial regions under the hypothesis that the differences in directional encoding are due to differences in their inputs. Particularly, gRSC was found to receive more inputs from anterodorsal thalamus, which was previously shown to display global encoding (Jacob et al., 2017). In addition, gRSC, but not dRSC, received inputs from dorsal subiculum which is the main output structure of hippocampus. It is however unclear if place cell in hippocampus displayed local or global place encoding. The second question thus arises: Do place cells display local or global place encoding? As hippocampus is strongly coupled with gRSC, place cells were predicted to show a global representation similar to that in gRSC. Extracellular recording of place cells in an environment with two differentially scented, visually rotated compartments showed that no place cells that are sensitive to the local visual scene were found. Thus, place cells displayed global encoding. Together, these findings indicate that global encoding in gRSC may be a consequence of its stronger coupling with vestibular-directional nuclei and the hippocampal system, both of which displayed global encoding. In contrast, the local encoding observed in dRSC may reflect its structural disconnect from the global spatial network