Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome.

Dravet syndrome (DS) is a neurodevelopmental disorder due to pathogenic variants in SCN1A encoding the Nav1.1 sodium channel subunit, characterized by treatment-resistant epilepsy, temperature-sensitive seizures, developmental delay/intellectual disability with features of autism spectrum disorder, and increased risk of sudden death. Convergent data suggest hippocampal dentate gyrus (DG) pathology in DS (Scn1a+/-) mice. We performed two-photon calcium imaging in brain slice to uncover a profound dysfunction of filtering of perforant path input by DG in young adult Scn1a+/- mice. This was not due to dysfunction of DG parvalbumin inhibitory interneurons (PV-INs), which were only mildly impaired at this timepoint; however, we identified enhanced excitatory input to granule cells, suggesting that circuit dysfunction is due to excessive excitation rather than impaired inhibition. We confirmed that both optogenetic stimulation of entorhinal cortex and selective chemogenetic inhibition of DG PV-INs lowered seizure threshold in vivo in young adult Scn1a+/- mice. Optogenetic activation of PV-INs, on the other hand, normalized evoked responses in granule cells in vitro. These results establish the corticohippocampal circuit as a key locus of pathology in Scn1a+/- mice and suggest that PV-INs retain powerful inhibitory function and may be harnessed as a potential therapeutic approach toward seizure modulation.

The Projection Targets of Medium Spiny Neurons Govern Cocaine-Evoked Synaptic Plasticity in the Nucleus Accumbens.

We examine synaptic connectivity and cocaine-evoked plasticity at specific networks within the nucleus accumbens (NAc). We identify distinct subpopulations of D1+ medium spiny neurons (MSNs) that project to either the ventral pallidum (D1+VP) or the ventral tegmental area (D1+VTA). We show that inputs from the ventral hippocampus (vHPC), but not the basolateral amygdala (BLA), are initially biased onto D1+VTA MSNs. However, repeated cocaine exposure eliminates the bias of vHPC inputs onto D1+VTA MSNs, while strengthening BLA inputs onto D1+VP MSNs. Our results reveal that connectivity and plasticity depend on the specific inputs and outputs of D1+ MSNs and highlight the complexity of cocaine-evoked circuit level adaptations in the NAc.

Prefrontal Cortex Drives Distinct Projection Neurons in the Basolateral Amygdala.

The prefrontal cortex (PFC) regulates emotional behavior via top-down control of the basolateral amygdala (BLA). However, the influence of PFC inputs on the different projection pathways within the BLA remains largely unexplored. Here, we combine whole-cell recordings and optogenetics to study these cell-type specific connections in mouse BLA. We characterize PFC inputs onto three distinct populations of BLA neurons that project to the PFC, ventral hippocampus, or nucleus accumbens. We find that PFC-evoked synaptic responses are strongest at amygdala-cortical and amygdala-hippocampal neurons and much weaker at amygdala-striatal neurons. We assess the mechanisms for this targeting and conclude that it reflects fewer connections onto amygdala-striatal neurons. Given the similar intrinsic properties of these cells, this connectivity allows the PFC to preferentially activate amygdala-cortical and amygdala-hippocampal neurons. Together, our findings reveal how PFC inputs to the BLA selectively drive feedback projections to the PFC and feedforward projections to the hippocampus.

Inhibitory Gating of Basolateral Amygdala Inputs to the Prefrontal Cortex.

UNLABELLED: Interactions between the prefrontal cortex (PFC) and basolateral amygdala (BLA) regulate emotional behaviors. However, a circuit-level understanding of functional connections between these brain regions remains incomplete. The BLA sends prominent glutamatergic projections to the PFC, but the overall influence of these inputs is predominantly inhibitory. Here we combine targeted recordings and optogenetics to examine the synaptic underpinnings of this inhibition in the mouse infralimbic PFC. We find that BLA inputs preferentially target layer 2 corticoamygdala over neighboring corticostriatal neurons. However, these inputs make even stronger connections onto neighboring parvalbumin and somatostatin expressing interneurons. Inhibitory connections from these two populations of interneurons are also much stronger onto corticoamygdala neurons. Consequently, BLA inputs are able to drive robust feedforward inhibition via two parallel interneuron pathways. Moreover, the contributions of these interneurons shift during repetitive activity, due to differences in short-term synaptic dynamics. Thus, parvalbumin interneurons are activated at the start of stimulus trains, whereas somatostatin interneuron activation builds during these trains. Together, these results reveal how the BLA impacts the PFC through a complex interplay of direct excitation and feedforward inhibition. They also highlight the roles of targeted connections onto multiple projection neurons and interneurons in this cortical circuit. Our findings provide a mechanistic understanding for how the BLA can influence the PFC circuit, with important implications for how this circuit participates in the regulation of emotion. SIGNIFICANCE STATEMENT: The prefrontal cortex (PFC) and basolateral amygdala (BLA) interact to control emotional behaviors. Here we show that BLA inputs elicit direct excitation and feedforward inhibition of layer 2 projection neurons in infralimbic PFC. BLA inputs are much stronger at corticoamygdala neurons compared with nearby corticostriatal neurons. However, these inputs are even more powerful at parvalbumin and somatostatin expressing interneurons. BLA inputs thus activate two parallel inhibitory networks, whose contributions change during repetitive activity. Finally, connections from these interneurons are also more powerful at corticoamygdala neurons compared with corticostriatal neurons. Together, our results demonstrate how the BLA predominantly inhibits the PFC via a complex sequence involving multiple cell-type and input-specific connections.

Optical triggered seizures using a caged 4-Aminopyridine.

Animal models of epilepsy are critical not only for understanding the fundamental mechanism of epilepsy but also for testing the efficacy of new antiepileptic drugs and novel therapeutic interventions. Photorelease of caged molecules is widely used in biological research to control pharmacologic events with high spatio-temporal resolution. We developed a technique for in vivo optical triggering of neocortical seizures using a novel caged compound based on ruthenium photochemistry (RuBi-4AP). Epileptiform events in mouse cortex were induced with blue light in both whole brain and focal illumination. Multi-electrode array recording and optical techniques were used to characterize the propagation of these epileptic events, including interictal spikes, polyspikes, and ictal discharges. These results demonstrate a novel optically-triggered seizure model, with high spatio-temporal control, that could have widespread application in the investigation of ictal onset, propagation and to develop novel light-based therapeutic interventions.

Classification of neocortical interneurons using affinity propagation.

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

State-dependent function of neocortical chandelier cells.

Chandelier (axoaxonic) cells (ChCs) are a distinct group of GABAergic interneurons that innervate the axon initial segments of pyramidal cells. However, their circuit role and the function of their clearly defined anatomical specificity remain unclear. Recent work has demonstrated that chandelier cells can produce depolarizing GABAergic PSPs, occasionally driving postsynaptic targets to spike. On the other hand, other work suggests that ChCs are hyperpolarizing and may have an inhibitory role. These disparate functional effects may reflect heterogeneity among ChCs. Here, using brain slices from transgenic mouse strains, we first demonstrate that, across different neocortical areas and genetic backgrounds, upper Layer 2/3 ChCs belong to a single electrophysiologically and morphologically defined population, extensively sampling Layer 1 inputs with asymmetric dendrites. Consistent with being a single cell type, we find electrical coupling between ChCs. We then investigate the effect of chandelier cell activation on pyramidal neuron spiking in several conditions, ranging from the resting membrane potential to stimuli designed to approximate in vivo membrane potential dynamics. We find that under quiescent conditions, chandelier cells are capable of both promoting and inhibiting spike generation, depending on the postsynaptic membrane potential. However, during in vivo-like membrane potential fluctuations, the dominant postsynaptic effect was a strong inhibition. Thus, neocortical chandelier cells, even from within a homogeneous population, appear to play a dual role in the circuit, helping to activate quiescent pyramidal neurons, while at the same time inhibiting active ones.

Comparison between supervised and unsupervised classifications of neuronal cell types: a case study.

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a "benchmark," the test to automatically distinguish between pyramidal cells and interneurons, defining "ground truth" by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies.

Quantitative classification of somatostatin-positive neocortical interneurons identifies three interneuron subtypes.

Deciphering the circuitry of the neocortex requires knowledge of its components, making a systematic classification of neocortical neurons necessary. GABAergic interneurons contribute most of the morphological, electrophysiological and molecular diversity of the cortex, yet interneuron subtypes are still not well defined. To quantitatively identify classes of interneurons, 59 GFP-positive interneurons from a somatostatin-positive mouse line were characterized by whole-cell recordings and anatomical reconstructions. For each neuron, we measured a series of physiological and morphological variables and analyzed these data using unsupervised classification methods. PCA and cluster analysis of morphological variables revealed three groups of cells: one comprised of Martinotti cells, and two other groups of interneurons with short asymmetric axons targeting layers 2/3 and bending medially. PCA and cluster analysis of electrophysiological variables also revealed the existence of these three groups of neurons, particularly with respect to action potential time course. These different morphological and electrophysiological characteristics could make each of these three interneuron subtypes particularly suited for a different function within the cortical circuit.