Long-read transcript sequencing identifies differential isoform expression in the entorhinal cortex in a transgenic model of tau pathology.

Increasing evidence suggests that alternative splicing plays an important role in Alzheimer's disease (AD) pathology. We used long-read sequencing in combination with a novel bioinformatics tool (FICLE) to profile transcript diversity in the entorhinal cortex of female transgenic (TG) mice harboring a mutant form of human tau. Our analyses revealed hundreds of novel isoforms and identified differentially expressed transcripts - including specific isoforms of Apoe, App, Cd33, Clu, Fyn and Trem2 - associated with the development of tau pathology in TG mice. Subsequent profiling of the human cortex from AD individuals and controls revealed similar patterns of transcript diversity, including the upregulation of the dominant TREM2 isoform in AD paralleling the increased expression of the homologous transcript in TG mice. Our results highlight the importance of differential transcript usage, even in the absence of gene-level expression alterations, as a mechanism underpinning gene regulation in the development of AD neuropathology.

Predictive grid coding in the medial entorhinal cortex.

The entorhinal cortex represents allocentric spatial geometry and egocentric speed and heading information required for spatial navigation. However, it remains unclear whether it contributes to the prediction of an animal's future location. We discovered grid cells in the medial entorhinal cortex (MEC) that have grid fields representing future locations during goal-directed behavior. These predictive grid cells represented prospective spatial information by shifting their grid fields against the direction of travel. Predictive grid cells discharged at the trough phases of the hippocampal CA1 theta oscillation and, together with other types of grid cells, organized sequences of the trajectory from the current to future positions across each theta cycle. Our results suggest that the MEC provides a predictive map that supports forward planning in spatial navigation.

Nogo-A-mediated constraints on activity-dependent synaptic plasticity and associativity in rat hippocampal CA2 synapses.

Hippocampal area CA2 has garnered attention in recent times owing to its significant involvement in social memory and distinctive plasticity characteristics. Research has revealed that the CA2 region demonstrates a remarkable resistance to plasticity, particularly in the Schaffer Collateral (SC)-CA2 pathway. In this study we investigated the role of Nogo-A, a well-known axon growth inhibitor and more recently discovered plasticity regulator, in modulating plasticity within the CA2 region. The findings demonstrate that blocking Nogo-A in male rat hippocampal slices facilitates the establishment of both short-term and long-term plasticity in the SC-CA2 pathway, while having no impact on the Entorhinal Cortical (EC)-CA2 pathway. Additionally, the study reveals that inhibiting Nogo-A enables association between the SC and EC pathways. Mechanistically, we confirm that Nogo-A operates through its well-known co-receptor, p75 neurotrophin receptor (p75NTR), and its downstream signaling factor such as Rho-associated protein kinase (ROCK), as their inhibition also allows plasticity induction in the SC-CA2 pathway. Additionally, the induction of long-term depression (LTD) in both the EC and SC-CA2 pathways led to persistent LTD, which was not affected by Nogo-A inhibition. Our study demonstrates the involvement of Nogo-A mediated signaling mechanisms in limiting synaptic plasticity within the CA2 region.

17ß-Estradiol reduces inhibitory synaptic currents in entorhinal cortex neurons through G protein-coupled estrogen receptor-1 activation of extracellular signal-regulated kinase.

Estrogens are believed to modulate cognitive functions in part through the modulation of synaptic transmission in the cortex and hippocampus. Administration of 17ß-estradiol (E2) can rapidly enhance excitatory synaptic transmission in the hippocampus and facilitate excitatory synaptic transmission in rat lateral entorhinal cortex via activation of the G protein-coupled estrogen receptor-1 (GPER1). To assess the mechanisms through which GPER1 activation facilitates synaptic transmission, we assessed the effects of acute 10 nM E2 administration on pharmacologically isolated evoked excitatory and inhibitory synaptic currents in layer II/III entorhinal neurons. Female Long-Evans rats were ovariectomized between postnatal day (PD) 63 and 74 and implanted with a subdermal E2 capsule to maintain continuous low levels of E2. Electrophysiological recordings were obtained between 7 and 20 days after ovariectomy. Application of E2 for 20 min did not significantly affect AMPA or NMDA receptor-mediated excitatory synaptic currents. However, GABA receptor-mediated inhibitory synaptic currents (IPSCs) were markedly reduced by E2 and returned towards baseline levels during the 20-min washout period. The inhibition of GABA-mediated IPSCs was blocked in the presence of the GPER1 receptor antagonist G15. GPER1 can modulate protein kinase A (PKA), but blocking PKA with intracellular KT5720 did not prevent the E2-induced reduction in IPSCs. GPER1 can also stimulate extracellular signal-regulated kinase (ERK), a negative modulator of GABAA receptors, and blocking activation of ERK with PD90859 prevented the E2-induced reduction of IPSCs. E2 can therefore result in a rapid GPER1 and ERK signaling-mediated reduction in GABA-mediated IPSCs. This provides a novel mechanism through which E2 can rapidly modulate synaptic excitability in entorhinal layer II/III neurons and may also contribute to E2 and ERK-dependent alterations in synaptic transmission in other brain areas.

Determinantal point process attention over grid cell code supports out of distribution generalization.

Deep neural networks have made tremendous gains in emulating human-like intelligence, and have been used increasingly as ways of understanding how the brain may solve the complex computational problems on which this relies. However, these still fall short of, and therefore fail to provide insight into how the brain supports strong forms of generalization of which humans are capable. One such case is out-of-distribution (OOD) generalization - successful performance on test examples that lie outside the distribution of the training set. Here, we identify properties of processing in the brain that may contribute to this ability. We describe a two-part algorithm that draws on specific features of neural computation to achieve OOD generalization, and provide a proof of concept by evaluating performance on two challenging cognitive tasks. First we draw on the fact that the mammalian brain represents metric spaces using grid cell code (e.g., in the entorhinal cortex): abstract representations of relational structure, organized in recurring motifs that cover the representational space. Second, we propose an attentional mechanism that operates over the grid cell code using determinantal point process (DPP), that we call DPP attention (DPP-A) - a transformation that ensures maximum sparseness in the coverage of that space. We show that a loss function that combines standard task-optimized error with DPP-A can exploit the recurring motifs in the grid cell code, and can be integrated with common architectures to achieve strong OOD generalization performance on analogy and arithmetic tasks. This provides both an interpretation of how the grid cell code in the mammalian brain may contribute to generalization performance, and at the same time a potential means for improving such capabilities in artificial neural networks.

Circuit dynamics of the olfactory pathway during olfactory learning.

The olfactory system plays crucial roles in perceiving and interacting with their surroundings. Previous studies have deciphered basic odor perceptions, but how information processing in the olfactory system is associated with learning and memory is poorly understood. In this review, we summarize recent studies on the anatomy and functional dynamics of the mouse olfactory learning pathway, focusing on how neuronal circuits in the olfactory bulb (OB) and olfactory cortical areas integrate odor information in learning. We also highlight in vivo evidence for the role of the lateral entorhinal cortex (LEC) in olfactory learning. Altogether, these studies demonstrate that brain regions throughout the olfactory system are critically involved in forming and representing learned knowledge. The role of olfactory areas in learning and memory, and their susceptibility to dysfunction in neurodegenerative diseases, necessitate further research.

Morphological and Functional Alterations in the CA1 Pyramidal Neurons of the Rat Hippocampus in the Chronic Phase of the Lithium-Pilocarpine Model of Epilepsy.

Epilepsy is known to cause alterations in neural networks. However, many details of these changes remain poorly understood. The objective of this study was to investigate changes in the properties of hippocampal CA1 pyramidal neurons and their synaptic inputs in a rat lithium-pilocarpine model of epilepsy. In the chronic phase of the model, we found a marked loss of pyramidal neurons in the CA1 area. However, the membrane properties of the neurons remained essentially unaltered. The results of the electrophysiological and morphological studies indicate that the direct pathway from the entorhinal cortex to CA1 neurons is reinforced in epileptic animals, whereas the inputs to them from CA3 are either unaltered or even diminished. In particular, the dendritic spine density in the str. lacunosum moleculare, where the direct pathway from the entorhinal cortex terminates, was found to be 2.5 times higher in epileptic rats than in control rats. Furthermore, the summation of responses upon stimulation of the temporoammonic pathway was enhanced by approximately twofold in epileptic rats. This enhancement is believed to be a significant contributing factor to the heightened epileptic activity observed in the entorhinal cortex of epileptic rats using an ex vivo 4-aminopyridine model.

Retrosplenial inputs drive visual representations in the medial entorhinal cortex.

The importance of visual cues for navigation and goal-directed behavior is well established, although the neural mechanisms supporting sensory representations in navigational circuits are largely unknown. Navigation is fundamentally dependent on the medial entorhinal cortex (MEC), which receives direct projections from neocortical visual areas, including the retrosplenial cortex (RSC). Here, we perform high-density recordings of MEC neurons in awake, head-fixed mice presented with simple visual stimuli and assess the dynamics of sensory-evoked activity. We find that a large fraction of neurons exhibit robust responses to visual input. Visually responsive cells are located primarily in layer 3 of the dorsal MEC and can be separated into subgroups based on functional and molecular properties. Furthermore, optogenetic suppression of RSC afferents within the MEC strongly reduces visual responses. Overall, our results demonstrate that the MEC can encode simple visual cues in the environment that may contribute to neural representations of location necessary for accurate navigation.

Border cells without theta rhythmicity in the medial prefrontal cortex.

The medial prefrontal cortex (mPFC) is a key brain structure for higher cognitive functions such as decision-making and goal-directed behavior, many of which require awareness of spatial variables including one's current position within the surrounding environment. Although previous studies have reported spatially tuned activities in mPFC during memory-related trajectory, the spatial tuning of mPFC network during freely foraging behavior remains elusive. Here, we reveal geometric border or border-proximal representations from the neural activity of mPFC ensembles during naturally exploring behavior, with both allocentric and egocentric boundary responses. Unlike most of classical border cells in the medial entorhinal cortex (MEC) discharging along a single wall, a large majority of border cells in mPFC fire particularly along four walls. mPFC border cells generate new firing fields to external insert, and remain stable under darkness, across distinct shapes, and in novel environments. In contrast to hippocampal theta entrainment during spatial working memory tasks, mPFC border cells rarely exhibited theta rhythmicity during spontaneous locomotion behavior. These findings reveal spatially modulated activity in mPFC, supporting local computation for cognitive functions involving spatial context and contributing to a broad spatial tuning property of cortical circuits.

Estimating individual trajectories of structural and cognitive decline in mild cognitive impairment for early prediction of progression to dementia of the Alzheimer's type.

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

A Continuous Attractor Model with Realistic Neural and Synaptic Properties Quantitatively Reproduces Grid Cell Physiology.

Computational simulations with data-driven physiological detail can foster a deeper understanding of the neural mechanisms involved in cognition. Here, we utilize the wealth of cellular properties from Hippocampome.org to study neural mechanisms of spatial coding with a spiking continuous attractor network model of medial entorhinal cortex circuit activity. The primary goal is to investigate if adding such realistic constraints could produce firing patterns similar to those measured in real neurons. Biological characteristics included in the work are excitability, connectivity, and synaptic signaling of neuron types defined primarily by their axonal and dendritic morphologies. We investigate the spiking dynamics in specific neuron types and the synaptic activities between groups of neurons. Modeling the rodent hippocampal formation keeps the simulations to a computationally reasonable scale while also anchoring the parameters and results to experimental measurements. Our model generates grid cell activity that well matches the spacing, size, and firing rates of grid fields recorded in live behaving animals from both published datasets and new experiments performed for this study. Our simulations also recreate different scales of those properties, e.g., small and large, as found along the dorsoventral axis of the medial entorhinal cortex. Computational exploration of neuronal and synaptic model parameters reveals that a broad range of neural properties produce grid fields in the simulation. These results demonstrate that the continuous attractor network model of grid cells is compatible with a spiking neural network implementation sourcing data-driven biophysical and anatomical parameters from Hippocampome.org. The software (version 1.0) is released as open source to enable broad community reuse and encourage novel applications.

Low rate hippocampal delay period activity encodes behavioral experience.

Remembering what just happened is a crucial prerequisite to form long-term memories but also for establishing and maintaining working memory. So far there is no general agreement about cortical mechanisms that support short-term memory. Using a classifier-based decoding approach, we report that hippocampal activity during few sparsely distributed brief time intervals contains information about the previous sensory motor experience of rodents. These intervals are characterized by only a small increase of firing rate of only a few neurons. These low-rate predictive patterns are present in both working memory and non-working memory tasks, in two rodent species, rats and Mongolian gerbils, are strongly reduced for rats with medial entorhinal cortex lesions, and depend on the familiarity of the sensory-motor context.

Reelin differentially shapes dendrite morphology of medial entorhinal cortical ocean and island cells.

The function of medial entorhinal cortex layer II (MECII) excitatory neurons has been recently explored. MECII dysfunction underlies deficits in spatial navigation and working memory. MECII neurons comprise two major excitatory neuronal populations, pyramidal island and stellate ocean cells, in addition to the inhibitory interneurons. Ocean cells express reelin and surround clusters of island cells that lack reelin expression. The influence of reelin expression by ocean cells and interneurons on their own morphological differentiation and that of MECII island cells has remained unknown. To address this, we used a conditional reelin knockout (RelncKO) mouse to induce reelin deficiency postnatally in vitro and in vivo. Reelin deficiency caused dendritic hypertrophy of ocean cells, interneurons and only proximal dendritic compartments of island cells. Ca2+ recording showed that both cell types exhibited an elevation of calcium frequencies in RelncKO, indicating that the hypertrophic effect is related to excessive Ca2+ signalling. Moreover, pharmacological receptor blockade in RelncKO mouse revealed malfunctioning of GABAB, NMDA and AMPA receptors. Collectively, this study emphasizes the significance of reelin in neuronal growth, and its absence results in dendrite hypertrophy of MECII neurons.

Mental navigation in the primate entorhinal cortex.

A cognitive map is a suitably structured representation that enables novel computations using previous experience; for example, planning a new route in a familiar space1. Work in mammals has found direct evidence for such representations in the presence of exogenous sensory inputs in both spatial2,3 and non-spatial domains4-10. Here we tested a foundational postulate of the original cognitive map theory1,11: that cognitive maps support endogenous computations without external input. We recorded from the entorhinal cortex of monkeys in a mental navigation task that required the monkeys to use a joystick to produce one-dimensional vectors between pairs of visual landmarks without seeing the intermediate landmarks. The ability of the monkeys to perform the task and generalize to new pairs indicated that they relied on a structured representation of the landmarks. Task-modulated neurons exhibited periodicity and ramping that matched the temporal structure of the landmarks and showed signatures of continuous attractor networks12,13. A continuous attractor network model of path integration14 augmented with a Hebbian-like learning mechanism provided an explanation of how the system could endogenously recall landmarks. The model also made an unexpected prediction that endogenous landmarks transiently slow path integration, reset the dynamics and thereby reduce variability. This prediction was borne out in a reanalysis of firing rate variability and behaviour. Our findings link the structured patterns of activity in the entorhinal cortex to the endogenous recruitment of a cognitive map during mental navigation.

Predictive sequence learning in the hippocampal formation.

The hippocampus receives sequences of sensory inputs from the cortex during exploration and encodes the sequences with millisecond precision. We developed a predictive autoencoder model of the hippocampus including the trisynaptic and monosynaptic circuits from the entorhinal cortex (EC). CA3 was trained as a self-supervised recurrent neural network to predict its next input. We confirmed that CA3 is predicting ahead by analyzing the spike coupling between simultaneously recorded neurons in the dentate gyrus, CA3, and CA1 of the mouse hippocampus. In the model, CA1 neurons signal prediction errors by comparing CA3 predictions to the next direct EC input. The model exhibits the rapid appearance and slow fading of CA1 place cells and displays replay and phase precession from CA3. The model could be learned in a biologically plausible way with error-encoding neurons. Similarities between the hippocampal and thalamocortical circuits suggest that such computation motif could also underlie self-supervised sequence learning in the cortex.

Entorhinal vessel density correlates with phosphorylated tau and TDP-43 pathology.

INTRODUCTION: The entorhinal cortex (EC) and perirhinal cortex (PC) are vulnerable to Alzheimer's disease. A triggering factor may be the interaction of vascular dysfunction and tau pathology. METHODS: We imaged post mortem human tissue at 100 µm3 with 7 T magnetic resonance imaging and manually labeled individual blood vessels (mean = 270 slices/case). Vessel density was quantified and compared per EC subfield, between EC and PC, and in relation to tau and TAR DNA-binding protein 43 (TDP-43) semiquantitative scores. RESULTS: PC was more vascularized than EC and vessel densities were higher in posterior EC subfields. Tau and TDP-43 strongly correlated with vasculature density and subregions with severe tau at the preclinical stage had significantly greater vessel density than those with low tau burden. DISCUSSION: These data impact cerebrovascular maps, quantification of subfield vasculature, and correlation of vasculature and pathology at early stages. The ordered association of vessel density, and tau or TDP-43 pathology, may be exploited in a predictive context. HIGHLIGHTS: Vessel density correlates with phosphorylated tau (p-tau) burden in entorhinal and perirhinal cortices. Perirhinal area 35 and posterior entorhinal cortex showed greatest p-tau burden but also the highest vessel density in the preclinical phase of Alzheimer's disease. We combined an ex vivo magnetic resonance imaging model and histopathology to demonstrate the 3D reconstruction of intracortical vessels and its spatial relationship to the pathology.

Persistent Interruption in Parvalbumin-Positive Inhibitory Interneurons: Biophysical and Mathematical Mechanisms.

Persistent activity in excitatory pyramidal cells (PYRs) is a putative mechanism for maintaining memory traces during working memory. We have recently demonstrated persistent interruption of firing in fast-spiking parvalbumin-expressing interneurons (PV-INs), a phenomenon that could serve as a substrate for persistent activity in PYRs through disinhibition lasting hundreds of milliseconds. Here, we find that hippocampal CA1 PV-INs exhibit type 2 excitability, like striatal and neocortical PV-INs. Modeling and mathematical analysis showed that the slowly inactivating potassium current KV1 contributes to type 2 excitability, enables the multiple firing regimes observed experimentally in PV-INs, and provides a mechanism for robust persistent interruption of firing. Using a fast/slow separation of times scales approach with the KV1 inactivation variable as a bifurcation parameter shows that the initial inhibitory stimulus stops repetitive firing by moving the membrane potential trajectory onto a coexisting stable fixed point corresponding to a nonspiking quiescent state. As KV1 inactivation decays, the trajectory follows the branch of stable fixed points until it crosses a subcritical Hopf bifurcation (HB) and then spirals out into repetitive firing. In a model describing entorhinal cortical PV-INs without KV1, interruption of firing could be achieved by taking advantage of the bistability inherent in type 2 excitability based on a subcritical HB, but the interruption was not robust to noise. Persistent interruption of firing is therefore broadly applicable to PV-INs in different brain regions but is only made robust to noise in the presence of a slow variable, KV1 inactivation.

Prefrontal coding of learned and inferred knowledge during REM and NREM sleep.

Idling brain activity has been proposed to facilitate inference, insight, and innovative problem-solving. However, it remains unclear how and when the idling brain can create novel ideas. Here, we show that cortical offline activity is both necessary and sufficient for building unlearned inferential knowledge from previously acquired information. In a transitive inference paradigm, male C57BL/6J mice gained the inference 1 day after, but not shortly after, complete training. Inhibiting the neuronal computations in the anterior cingulate cortex (ACC) during post-learning either non-rapid eye movement (NREM) or rapid eye movement (REM) sleep, but not wakefulness, disrupted the inference without affecting the learned knowledge. In vivo Ca2+ imaging suggests that NREM sleep organizes the scattered learned knowledge in a complete hierarchy, while REM sleep computes the inferential information from the organized hierarchy. Furthermore, after insufficient learning, artificial activation of medial entorhinal cortex-ACC dialog during only REM sleep created inferential knowledge. Collectively, our study provides a mechanistic insight on NREM and REM coordination in weaving inferential knowledge, thus highlighting the power of idling brain in cognitive flexibility.

Dynamics of spike transmission and suppression between principal cells and interneurons in the hippocampus and entorhinal cortex.

Synaptic excitation and inhibition are essential for neuronal communication. However, the variables that regulate synaptic excitation and inhibition in the intact brain remain largely unknown. Here, we examined how spike transmission and suppression between principal cells (PCs) and interneurons (INTs) are modulated by activity history, brain state, cell type, and somatic distance between presynaptic and postsynaptic neurons by applying cross-correlogram analyses to datasets recorded from the dorsal hippocampus and medial entorhinal cortex (MEC) of 11 male behaving and sleeping Long Evans rats. The strength, temporal delay, and brain-state dependency of the spike transmission and suppression depended on the subregions/layers. The spike transmission probability of PC-INT excitatory pairs that showed short-term depression versus short-term facilitation was higher in CA1 and lower in CA3. Likewise, the intersomatic distance affected the proportion of PC-INT excitatory pairs that showed short-term depression and facilitation in the opposite manner in CA1 compared with CA3. The time constant of depression was longer, while that of facilitation was shorter in MEC than in CA1 and CA3. During sharp-wave ripples, spike transmission showed a larger gain in the MEC than in CA1 and CA3. The intersomatic distance affected the spike transmission gain during sharp-wave ripples differently in CA1 versus CA3. A subgroup of MEC layer 3 (EC3) INTs preferentially received excitatory inputs from and inhibited MEC layer 2 (EC2) PCs. The EC2 PC-EC3 INT excitatory pairs, most of which showed short-term depression, exhibited higher spike transmission probabilities than the EC2 PC-EC2 INT and EC3 PC-EC3 INT excitatory pairs. EC2 putative stellate cells exhibited stronger spike transmission to and received weaker spike suppression from EC3 INTs than EC2 putative pyramidal cells. This study provides detailed comparisons of monosynaptic interaction dynamics in the hippocampal-entorhinal loop, which may help to elucidate circuit operations.

Medial entorhinal cortex mediates learning of context-dependent interval timing behavior.

Episodic memory requires encoding the temporal structure of experience and relies on brain circuits in the medial temporal lobe, including the medial entorhinal cortex (MEC). Recent studies have identified MEC 'time cells', which fire at specific moments during interval timing tasks, collectively tiling the entire timing period. It has been hypothesized that MEC time cells could provide temporal information necessary for episodic memories, yet it remains unknown whether they display learning dynamics required for encoding different temporal contexts. To explore this, we developed a new behavioral paradigm requiring mice to distinguish temporal contexts. Combined with methods for cellular resolution calcium imaging, we found that MEC time cells display context-dependent neural activity that emerges with task learning. Through chemogenetic inactivation we found that MEC activity is necessary for learning of context-dependent interval timing behavior. Finally, we found evidence of a common circuit mechanism that could drive sequential activity of both time cells and spatially selective neurons in MEC. Our work suggests that the clock-like firing of MEC time cells can be modulated by learning, allowing the tracking of various temporal structures that emerge through experience.