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

Abstract

fragile X syndrome is a neurodevelopmental disorder associated with a broad range of neural phenotypes. Interpreting these findings has proven challenging because some phenotypes may reflect compensatory mechanisms or normal forms of plasticity differentially engaged by experiential differences. To help minimize compensatory and experiential influences, we used an ex vivo approach to study network dynamics and plasticity of cortical microcircuits. in Fmr1 −/y circuits, the spatiotemporal structure of Up-states was less reproducible, suggesting alterations in the plasticity mechanisms governing network activity. chronic optical stimulation revealed normal homeostatic plasticity of Up-states, however, Fmr1 −/y circuits exhibited abnormal experience-dependent plasticity as they did not adapt to chronically presented temporal patterns in an interval-specific manner. These results, suggest that while homeostatic plasticity is normal, Fmr1 −/y circuits exhibit deficits in the ability to orchestrate multiple forms of synaptic plasticity and to adapt to sensory patterns in an experience-dependent manner-which is likely to contribute to learning deficits. Fragile X syndrome (FXS) is the leading monogenic cause of autism and intellectual disabilities, and reflects loss of function mutations in the RNA binding protein, Fragile X Mental Retardation Protein (FMRP) 1-4. Since the generation of the first mouse model of FXS 5 , a large number of neural phenotypes have been associated with the syndrome including: abnormalities in dendritic spine morphology and stabilization 6-12 , altered shortand long-term forms of synaptic plasticity 13-20 , abnormal axonal development 19,21,22 , changes in interneuronal connectivity 23,24 , channelopathies 16,25-31 , and imbalanced excitation/inhibition 24,32-44. The sheer diversity of reported neural phenotypes highlights the challenge in determining which phenotypes are a primary consequence of the absence of FMRP, from those that reflect indirect secondary neural phenotypes. These secondary neural phenotypes could reflect: (1) compensatory and homeostatic mechanisms, i.e., genetic redundancy and homeostatic plasticity engaged to compensate for the primary consequences of the lack of FMRP 44-46 ; or (2) normal forms of plasticity shaped by experiential differences, including differences in sensory exploration, social interactions and maternal care. Indeed, it is increasingly recognized that some of the neural phenotypes reported in animal models of FXS and autism spectrum disorder (ASD) may reflect compensatory mechanisms or differential developmental experiences 44,45. For example, it is well established that early differences in sensory experience, social interactions and maternal rearing, can alter numerous neural properties throughout life 47-53. As a result of decreased social interactions, young FMRP-deficient animals could in effect inhabit an impoverished sensory environment. Indeed, it is also well established that in mouse models of FXS and ASD, animals experience differences in so...

Differential excitability of PV and SST neurons results in distinct functional roles in inhibition stabilization of Up-states

“…Ex vivo organotypic slices provide a manner to unambiguously ascertain that the observed dynamics emerge locally within the circuit being studied. Additionally, consistent with the notion that Up-states reflect a core dynamic regime that cortical circuits are programmed to seek out, spontaneous and evoked Up-states emerge in cortical organotypic cultures over the course of ex vivo development (Johnson and Buonomano, 2007; Motanis and Buonomano, 2015; Motanis and Buonomano, 2020). Overall, our approach allowed us to directly compare experimental and model parameters of PV and SST neurons.…”

“…The best-studied form of emergent dynamics in neocortical circuits are Up-states, a term that generally refers to network-wide regimes in which excitatory neurons can transition between quiescent Down-states to more-or-less stable depolarized states with low to moderate firing rates (Sanchez-Vives and McCormick, 2000; Neske et al, 2015; Bartram et al, 2017). Up-states can occur spontaneously or be evoked and are observed in vivo during anesthesia, slow-wave sleep, quiet wakefulness (Steriade et al, 1993; Timofeev et al, 2000; Beltramo et al, 2013; Hromádka et al, 2013), and in acute slices (Sanchez-Vives and McCormick, 2000; Shu et al, 2003; Fanselow and Connors, 2010; Sippy and Yuste, 2013; Xu et al, 2013; Sadovsky and MacLean, 2014; Neske et al, 2015; Bartram et al, 2017), as well as in ex vivo cortical cultures (Plenz and Kitai, 1998; Seamans et al, 2003; Johnson and Buonomano, 2007; Kroener et al, 2009; Motanis and Buonomano, 2015; Motanis and Buonomano, 2020). Up-states have been proposed to have multiple functional roles, including memory consolidation and synaptic homeostasis (Tononi and Cirelli, 2003; Marshall et al, 2006; Sirota and Buzsáki, 2007; Vyazovskiy et al, 2008; Diekelmann and Born, 2010).…”

Orchestrated Excitatory and Inhibitory Learning Rules Lead to the Unsupervised Emergence of Self-sustained and Inhibition-stabilized Dynamics

“…During Up-states the firing rate of excitatory neurons is relatively low (1-5 Hz) indicating that recurrent excitation is held in check by appropriately tuned inhibition (Neske et al, 2015;Jercog et al, 2017;Romero-Sosa et al, 2020). By recording in organotypic cortical circuits it is possible to show that Up-states develop over the course of ex vivo development (Plenz and Kitai, 1998;Seamans et al, 2003;Johnson and Buonomano, 2007;Kroener et al, 2009;Motanis and Buonomano, 2015;Motanis and Buonomano, 2020). Early in development, at 8 days-invitro (DIV-8) most of the neurons are silent, while at later stages (DIV-23) spontaneous Up-states are observed (Fig.…”

“…Here we focused on the emergence of Up-states because: 1) they represent one of the best examples of both self-sustained dynamics and of an inhibition-stabilized network regime; and 2) Up-states emerge autonomously during ex vivo development, and are homeostatically modulated by external activity (Plenz and Kitai, 1998;Seamans et al, 2003;Johnson and Buonomano, 2007;Kroener et al, 2009;Motanis and Buonomano, 2015;Motanis and Buonomano, 2020)-thus suggesting that there are homeostatic learning rules in place to guide cortical networks to exhibit Up-states.…”

“…One attractive possibility is that cortical neurons are homeostatically programmed to generate Up-states. Specifically, that both excitatory and inhibitory neurons exhibit ontogenetically programmed firing rate setpoints, and they homeostatically adjust their excitatory and inhibitory weights to reach these target setpoints (Motanis and Buonomano, 2015;Motanis and Buonomano, 2020). Numerous homeostatic synaptic learning rules have been proposed based on experimental findings.…”