Somatostatin expressing GABAergic interneurons in the medial entorhinal cortex preferentially inhibit layerIII-V pyramidal cells

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.

A non-canonical visual cortical-entorhinal pathway contributes to spatial navigation

Visual information is important for accurate spatial coding and memory-guided navigation. As a crucial area for spatial cognition, the medial entorhinal cortex (MEC) harbors diverse spatially tuned cells and functions as the major gateway relaying sensory inputs to the hippocampus containing place cells. However, how visual information enters the MEC has not been fully understood. Here, we identify a pathway originating in the secondary visual cortex (V2) and directly targeting MEC layer 5a (L5a). L5a neurons served as a network hub for visual processing in the MEC by routing visual inputs from multiple V2 areas to other local neurons and hippocampal CA1. Interrupting this pathway severely impaired visual stimulus-evoked neural activity in the MEC and performance of mice in navigation tasks. These observations reveal a visual cortical-entorhinal pathway highlighting the role of MEC L5a in sensory information transmission, a function typically attributed to MEC superficial layers before.

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 was 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 is released as open source to enable broad community reuse and encourage novel applications.

A neuronal coping mechanism linking stress-induced anxiety to motivation for reward

Stress coping involves innate and active motivational behaviors that reduce anxiety under stressful situations. However, the neuronal bases directly linking stress, anxiety, and motivation are largely unknown. Here, we show that acute stressors activate mouse GABAergic neurons in the interpeduncular nucleus (IPN). Stress-coping behavior including self-grooming and reward behavior including sucrose consumption inherently reduced IPN GABAergic neuron activity. Optogenetic silencing of IPN GABAergic neuron activation during acute stress episodes mimicked coping strategies and alleviated anxiety-like behavior. In a mouse model of stress-enhanced motivation for sucrose seeking, photoinhibition of IPN GABAergic neurons reduced stress-induced motivation for sucrose, whereas photoactivation of IPN GABAergic neurons or excitatory inputs from medial habenula potentiated sucrose seeking. Single-cell sequencing, fiber photometry, and optogenetic experiments revealed that stress-activated IPN GABAergic neurons that drive motivated sucrose seeking express somatostatin. Together, these data suggest that stress induces innate behaviors and motivates reward seeking to oppose IPN neuronal activation as an anxiolytic stress-coping mechanism.

Chronic stress, neuroinflammation, and depression: an overview of pathophysiological mechanisms and emerging anti-inflammatories

In a subset of patients, chronic exposure to stress is an etiological risk factor for neuroinflammation and depression. Neuroinflammation affects up to 27% of patients with MDD and is associated with a more severe, chronic, and treatment-resistant trajectory. Inflammation is not unique to depression and has transdiagnostic effects suggesting a shared etiological risk factor underlying psychopathologies and metabolic disorders. Research supports an association but not necessarily a causation with depression. Putative mechanisms link chronic stress to dysregulation of the HPA axis and immune cell glucocorticoid resistance resulting in hyperactivation of the peripheral immune system. The chronic extracellular release of DAMPs and immune cell DAMP-PRR signaling creates a feed forward loop that accelerates peripheral and central inflammation. Higher plasma levels of inflammatory cytokines, most consistently interleukin IL-1β, IL-6, and TNF-α, are correlated with greater depressive symptomatology. Cytokines sensitize the HPA axis, disrupt the negative feedback loop, and further propagate inflammatory reactions. Peripheral inflammation exacerbates central inflammation (neuroinflammation) through several mechanisms including disruption of the blood-brain barrier, immune cellular trafficking, and activation of glial cells. Activated glial cells release cytokines, chemokines, and reactive oxygen and nitrogen species into the extra-synaptic space dysregulating neurotransmitter systems, imbalancing the excitatory to inhibitory ratio, and disrupting neural circuitry plasticity and adaptation. In particular, microglial activation and toxicity plays a central role in the pathophysiology of neuroinflammation. Magnetic resonance imaging (MRI) studies most consistently show reduced hippocampal volumes. Neural circuitry dysfunction such as hypoactivation between the ventral striatum and the ventromedial prefrontal cortex underlies the melancholic phenotype of depression. Chronic administration of monoamine-based antidepressants counters the inflammatory response, but with a delayed therapeutic onset. Therapeutics targeting cell mediated immunity, generalized and specific inflammatory signaling pathways, and nitro-oxidative stress have enormous potential to advance the treatment landscape. Future clinical trials will need to include immune system perturbations as biomarker outcome measures to facilitate novel antidepressant development. In this overview, we explore the inflammatory correlates of depression and elucidate pathomechanisms to facilitate the development of novel biomarkers and therapeutics.

The hippocampus associated GABAergic neural network impairment in early-stage of Alzheimer's disease

Alzheimer's disease (AD) is the commonest neurodegenerative disease with slow progression. Pieces of evidence suggest that the GABAergic system is impaired in the early stage of AD, leading to hippocampal neuron over-activity and further leading to memory and cognitive impairment in patients with AD. However, the precise impairment mechanism of the GABAergic system on the pathogenesis of AD is still unclear. The impairment of neural networks associated with the GABAergic system is tightly associated with AD. Therefore, we describe the roles played by hippocampus-related GABAergic circuits and their impairments in AD neuropathology. In addition, we give our understand on the process from GABAergic circuit impairment to cognitive and memory impairment, since recent studies on astrocyte in AD plays an important role behind cognition dysfunction caused by GABAergic circuit impairment, which helps better understand the GABAergic system and could open up innovative AD therapy.

Involvement of GABAergic Interneuron Subtypes in 4-Aminopyridine-Induced Seizure-Like Events in Mouse Entorhinal Cortex in Vitro

Single-unit recordings performed in temporal lobe epilepsy patients and in models of temporal lobe seizures have shown that interneurons are active at focal seizure onset. We performed simultaneous patch-clamp and field potential recordings in entorhinal cortex slices of GAD65 and GAD67 C57BL/6J male mice that express green fluorescent protein in GABAergic neurons to analyze the activity of specific interneuron (IN) subpopulations during acute seizure-like events (SLEs) induced by 4-aminopyridine (4-AP; 100 μm). IN subtypes were identified as parvalbuminergic (INPV, n = 17), cholecystokinergic (INCCK), n = 13], and somatostatinergic (INSOM, n = 15), according to neurophysiological features and single-cell digital PCR. INPV and INCCK discharged at the start of 4-AP-induced SLEs characterized by either low-voltage fast or hyper-synchronous onset pattern. In both SLE onset types, INSOM fired earliest before SLEs, followed by INPV and INCCK discharges. Pyramidal neurons became active with variable delays after SLE onset. Depolarizing block was observed in ∼50% of cells in each INs subgroup, and it was longer in IN (∼4 s) than in pyramidal neurons (<1 s). As SLE evolved, all IN subtypes generated action potential bursts synchronous with the field potential events leading to SLE termination. High-frequency firing throughout the SLE occurred in one-third of INPV and INSOM We conclude that entorhinal cortex INs are very active at the onset and during the progression of SLEs induced by 4-AP. These results support earlier in vivo and in vivo evidence and suggest that INs have a preferential role in focal seizure initiation and development.SIGNIFICANCE STATEMENT Focal seizures are believed to result from enhanced excitation. Nevertheless, we and others demonstrated that cortical GABAergic networks may initiate focal seizures. Here, we analyzed for the first time the role of different IN subtypes in seizures generated by 4-aminopyridine in the mouse entorhinal cortex slices. We found that in this in vitro focal seizure model, all IN types contribute to seizure initiation and that INs precede firing of principal cells. This evidence is in agreement with the active role of GABAergic networks in seizure generation.

Key role of neuronal diversity in structured reservoir computing

Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supervised manner. These models, however, are typically limited to randomly connected networks of homogeneous units. Here, we propose a new class of structured reservoir models that incorporates a diversity of cell types and their known connections. In a first version of the model, the reservoir was composed of mean-rate units separated into pyramidal, parvalbumin, and somatostatin cells. Stability analysis of this model revealed two distinct dynamical regimes, namely, (i) an inhibition-stabilized network (ISN) where strong recurrent excitation is balanced by strong inhibition and (ii) a non-ISN network with weak excitation. These results were extended to a leaky integrate-and-fire model that captured different cell types along with their network architecture. ISN and non-ISN reservoir networks were trained to relay and generate a chaotic Lorenz attractor. Despite their increased performance, ISN networks operate in a regime of activity near the limits of stability where external perturbations yield a rapid divergence in output. The proposed framework of structured reservoir computing opens avenues for exploring how neural microcircuits can balance performance and stability when representing time series through distinct dynamical regimes.

Neurogliaform cells mediate feedback inhibition in the medial entorhinal cortex

Layer I of the medial entorhinal cortex (MEC) contains converging axons from several brain areas and dendritic tufts originating from principal cells located in multiple layers. Moreover, specific GABAergic interneurons are also located in the area, but their inputs, outputs, and effect on local network events remain elusive. Neurogliaform cells are the most frequent and critically positioned inhibitory neurons in layer I. They are considered to conduct feed-forward inhibition via GABAA and GABAB receptors on pyramidal cells located in several cortical areas. Using optogenetic experiments, we showed that layer I neurogliaform cells receive excitatory inputs from layer II pyramidal cells, thereby playing a critical role in local feedback inhibition in the MEC. We also found that neurogliaform cells are evenly distributed in layer I and do not correlate with the previously described compartmentalization (“cell islands”) of layer II. We concluded that the activity of neurogliaform cells in layer I is largely set by layer II pyramidal cells through excitatory synapses, potentially inhibiting the apical dendrites of all types of principal cells in the MEC.

Neuropeptide System Regulation of Prefrontal Cortex Circuitry: Implications for Neuropsychiatric Disorders

Neuropeptides, a diverse class of signaling molecules in the nervous system, modulate various biological effects including membrane excitability, synaptic transmission and synaptogenesis, gene expression, and glial cell architecture and function. To date, most of what is known about neuropeptide action is limited to subcortical brain structures and tissue outside of the central nervous system. Thus, there is a knowledge gap in our understanding of neuropeptide function within cortical circuits. In this review, we provide a comprehensive overview of various families of neuropeptides and their cognate receptors that are expressed in the prefrontal cortex (PFC). Specifically, we highlight dynorphin, enkephalin, corticotropin-releasing factor, cholecystokinin, somatostatin, neuropeptide Y, and vasoactive intestinal peptide. Further, we review the implication of neuropeptide signaling in prefrontal cortical circuit function and use as potential therapeutic targets. Together, this review summarizes established knowledge and highlights unknowns of neuropeptide modulation of neural function underlying various biological effects while offering insights for future research. An increased emphasis in this area of study is necessary to elucidate basic principles of the diverse signaling molecules used in cortical circuits beyond fast excitatory and inhibitory transmitters as well as consider components of neuropeptide action in the PFC as a potential therapeutic target for neurological disorders. Therefore, this review not only sheds light on the importance of cortical neuropeptide studies, but also provides a comprehensive overview of neuropeptide action in the PFC to serve as a roadmap for future studies in this field.

Kinetics and Connectivity Properties of Parvalbumin- and Somatostatin-Positive Inhibition in Layer 2/3 Medial Entorhinal Cortex

Parvalbumin-positive (Pvalb⁺) and somatostatin-positive (Sst⁺) cells are the two largest subgroups of inhibitory interneurons. Studies in visual cortex indicate that synaptic connections between Pvalb⁺ cells are common while connections between Sst⁺ interneurons have not been observed. The inhibitory connectivity and kinetics of these two interneuron subpopulations, however, have not been characterized in medial entorhinal cortex (mEC). Using fluorescence-guided paired recordings in mouse brain slices from interneurons and excitatory cells in layer 2/3 mEC, we found that, unlike neocortical measures, Sst⁺ cells inhibit each other, albeit with a lower probability than Pvalb⁺ cells (18% vs 36% for unidirectional connections). Gap junction connections were also more frequent between Pvalb⁺ cells than between Sst⁺ cells. Pvalb⁺ cells inhibited each other with larger conductances, smaller decay time constants, and shorter delays. Similarly, synaptic connections between Pvalb⁺ and excitatory cells were more likely and expressed faster decay times and shorter delays than those between Sst⁺ and excitatory cells. Inhibitory cells exhibited smaller synaptic decay time constants between interneurons than on their excitatory targets. Inhibition between interneurons also depressed faster, and to a greater extent. Finally, inhibition onto layer 2 pyramidal and stellate cells originating from Pvalb⁺ interneurons were very similar, with no significant differences in connection likelihood, inhibitory amplitude, and decay time. A model of short-term depression fitted to the data indicates that recovery time constants for refilling the available pool are in the range of 50-150 ms and that the fraction of the available pool released on each spike is in the range 0.2-0.5.