Decreased reproducibility and abnormal experience-dependent plasticity of network dynamics in Fragile X circuits

Caveolin-1-Mediated Cholesterol Accumulation Contributes to Exaggerated mGluR-Dependent Long-Term Depression and Impaired Cognition in Fmr1 Knockout Mice

Fragile X syndrome (FXS) is one of the most common inherited mental retardation diseases and is caused by the loss of fragile X mental retardation protein (FMRP) expression. The metabotropic glutamate receptor (mGluR) theory of FXS states that enhanced mGluR-dependent long-term depression (LTD) due to FMRP loss is involved in aberrant synaptic plasticity and autistic-like behaviors, but little is known about the underlying molecular mechanism. Here, we found that only hippocampal mGluR-LTD was exaggerated in adolescent Fmr1 KO mice, while N-methyl-D-aspartate receptor (NMDAR)-LTD was intact in mice of all ages. This development-dependent alteration was related to the differential expression of caveolin-1 (Cav1), which is essential for caveolae formation. Knockdown of Cav1 restored the enhanced mGluR-LTD in Fmr1 KO mice. Moreover, hippocampal Cav1 expression in Fmr1 KO mice induced excessive endocytosis of the α-amino-3-hydroxyl-5-methyl-4-isoxazolepropionate (AMPA) receptor subunit GluA2. This process relied on mGluR1/5 activation rather than NMDAR. Interference with Cav1 expression reversed these changes. Furthermore, massive cholesterol accumulation contributed to redundant caveolae formation, which provided the platform for mGluR-triggered Cav1 coupling to GluA2. Importantly, injection of the cholesterol scavenger methyl-β-cyclodextrin (Mβ-CD) recovered AMPA receptor trafficking and markedly alleviated hyperactivity, hippocampus-dependent fear memory, and spatial memory defects in Fmr1 KO mice. Together, our findings elucidate the important role of Cav1 in mediating mGluR-LTD enhancement and further inducing AMPA receptor endocytosis and suggest that cholesterol depletion by Mβ-CD during caveolae formation may be a novel and safe strategy to treat FXS.

Creation of Neuronal Ensembles and Cell-Specific Homeostatic Plasticity through Chronic Sparse Optogenetic Stimulation

Cortical computations emerge from the dynamics of neurons embedded in complex cortical circuits. Within these circuits, neuronal ensembles, which represent subnetworks with shared functional connectivity, emerge in an experience-dependent manner. Here we induced ensembles in ex vivo cortical circuits from mice of either sex by differentially activating subpopulations through chronic optogenetic stimulation. We observed a decrease in voltage correlation, and importantly a synaptic decoupling between the stimulated and nonstimulated populations. We also observed a decrease in firing rate during Up-states in the stimulated population. These ensemble-specific changes were accompanied by decreases in intrinsic excitability in the stimulated population, and a decrease in connectivity between stimulated and nonstimulated pyramidal neurons. By incorporating the empirically observed changes in intrinsic excitability and connectivity into a spiking neural network model, we were able to demonstrate that changes in both intrinsic excitability and connectivity accounted for the decreased firing rate, but only changes in connectivity accounted for the observed decorrelation. Our findings help ascertain the mechanisms underlying the ability of chronic patterned stimulation to create ensembles within cortical circuits and, importantly, show that while Up-states are a global network-wide phenomenon, functionally distinct ensembles can preserve their identity during Up-states through differential firing rates and correlations.SIGNIFICANCE STATEMENT The connectivity and activity patterns of local cortical circuits are shaped by experience. This experience-dependent reorganization of cortical circuits is driven by complex interactions between different local learning rules, external input, and reciprocal feedback between many distinct brain areas. Here we used an ex vivo approach to demonstrate how simple forms of chronic external stimulation can shape local cortical circuits in terms of their correlated activity and functional connectivity. The absence of feedback between different brain areas and full control of external input allowed for a tractable system to study the underlying mechanisms and development of a computational model. Results show that differential stimulation of subpopulations of neurons significantly reshapes cortical circuits and forms subnetworks referred to as neuronal ensembles.

Paradoxical self-sustained dynamics emerge from orchestrated excitatory and inhibitory homeostatic plasticity rules

Cortical networks have the remarkable ability to self-assemble into dynamic regimes in which excitatory positive feedback is balanced by recurrent inhibition. This inhibition-stabilized regime is increasingly viewed as the default dynamic regime of the cortex, but how it emerges in an unsupervised manner remains unknown. We prove that classic forms of homeostatic plasticity are unable to drive recurrent networks to an inhibition-stabilized regime due to the well-known paradoxical effect. We next derive a novel family of cross-homeostatic rules that lead to the unsupervised emergence of inhibition-stabilized networks. These rules shed new light on how the brain may reach its default dynamic state and provide a valuable tool to self-assemble artificial neural networks into ideal computational regimes.

Self-sustained neural activity maintained through local recurrent connections is of fundamental importance to cortical function. Converging theoretical and experimental evidence indicates that cortical circuits generating self-sustained dynamics operate in an inhibition-stabilized regime. Theoretical work has established that four sets of weights (W_E←E, W_E←I, W_I←E, and W_I←I) must obey specific relationships to produce inhibition-stabilized dynamics, but it is not known how the brain can appropriately set the values of all four weight classes in an unsupervised manner to be in the inhibition-stabilized regime. We prove that standard homeostatic plasticity rules are generally unable to generate inhibition-stabilized dynamics and that their instability is caused by a signature property of inhibition-stabilized networks: the paradoxical effect. In contrast, we show that a family of “cross-homeostatic” rules overcome the paradoxical effect and robustly lead to the emergence of stable dynamics. This work provides a model of how—beginning from a silent network—self-sustained inhibition-stabilized dynamics can emerge from learning rules governing all four synaptic weight classes in an orchestrated manner.

Two-photon calcium imaging of neuronal activity

In vivo two-photon calcium imaging (2PCI) is a technique used for recording neuronal activity in the intact brain. It is based on the principle that, when neurons fire action potentials, intracellular calcium levels rise, which can be detected using fluorescent molecules that bind to calcium. This Primer is designed for scientists who are considering embarking on experiments with 2PCI. We provide the reader with a background on the basic concepts behind calcium imaging and on the reasons why 2PCI is an increasingly powerful and versatile technique in neuroscience. The Primer explains the different steps involved in experiments with 2PCI, provides examples of what ideal preparations should look like and explains how data are analysed. We also discuss some of the current limitations of the technique, and the types of solutions to circumvent them. Finally, we conclude by anticipating what the future of 2PCI might look like, emphasizing some of the analysis pipelines that are being developed and international efforts for data sharing.

Hyperexcitability and Homeostasis in Fragile X Syndrome

Fragile X Syndrome (FXS) is a leading inherited cause of autism and intellectual disability, resulting from a mutation in the FMR1 gene and subsequent loss of its protein product FMRP. Despite this simple genetic origin, FXS is a phenotypically complex disorder with a range of physical and neurocognitive disruptions. While numerous molecular and cellular pathways are affected by FMRP loss, there is growing evidence that circuit hyperexcitability may be a common convergence point that can account for many of the wide-ranging phenotypes seen in FXS. The mechanisms for hyperexcitability in FXS include alterations to excitatory synaptic function and connectivity, reduced inhibitory neuron activity, as well as changes to ion channel expression and conductance. However, understanding the impact of FMR1 mutation on circuit function is complicated by the inherent plasticity in neural circuits, which display an array of homeostatic mechanisms to maintain activity near set levels. FMRP is also an important regulator of activity-dependent plasticity in the brain, meaning that dysregulated plasticity can be both a cause and consequence of hyperexcitable networks in FXS. This makes it difficult to separate the direct effects of FMR1 mutation from the myriad and pleiotropic compensatory changes associated with it, both of which are likely to contribute to FXS pathophysiology. Here we will: (1) review evidence for hyperexcitability and homeostatic plasticity phenotypes in FXS models, focusing on similarities/differences across brain regions, cell-types, and developmental time points; (2) examine how excitability and plasticity disruptions interact with each other to ultimately contribute to circuit dysfunction in FXS; and (3) discuss how these synaptic and circuit deficits contribute to disease-relevant behavioral phenotypes like epilepsy and sensory hypersensitivity. Through this discussion of where the current field stands, we aim to introduce perspectives moving forward in FXS research.

Differential Excitability of PV and SST Neurons Results in Distinct Functional Roles in Inhibition Stabilization of Up States

Up states are the best studied example of an emergent neural dynamic regime. Computational models based on a single class of inhibitory neurons indicate that Up states reflect bistable dynamic systems in which positive feedback is stabilized by strong inhibition and predict a paradoxical effect in which increased drive to inhibitory neurons results in decreased inhibitory activity. To date, however, computational models have not incorporated empirically defined properties of parvalbumin (PV) and somatostatin (SST) neurons. Here we first experimentally characterized the frequency-current (F-I) curves of pyramidal (Pyr), PV, and SST neurons from mice of either sex, and confirmed a sharp difference between the threshold and slopes of PV and SST neurons. The empirically defined F-I curves were incorporated into a three-population computational model that simulated the empirically derived firing rates of pyramidal, PV, and SST neurons. Simulations revealed that the intrinsic properties were sufficient to predict that PV neurons are primarily responsible for generating the nontrivial fixed points representing Up states. Simulations and analytical methods demonstrated that while the paradoxical effect is not obligatory in a model with two classes of inhibitory neurons, it is present in most regimes. Finally, experimental tests validated predictions of the model that the Pyr ↔ PV inhibitory loop is stronger than the Pyr ↔ SST loop.SIGNIFICANCE STATEMENT Many cortical computations, such as working memory, rely on the local recurrent excitatory connections that define cortical circuit motifs. Up states are among the best studied examples of neural dynamic regimes that rely on recurrent excitatory excitation. However, this positive feedback must be held in check by inhibition. To address the relative contribution of PV and SST neurons, we characterized the intrinsic input-output differences between these classes of inhibitory neurons and, using experimental and theoretical methods, show that the higher threshold and gain of PV leads to a dominant role in network stabilization.