A Powerful DREADD: Revealing Structural Drivers of Functional Dynamics

Tet2 acts in the lateral habenula to regulate social preference in mice

The lateral habenula (LHb) has been considered a moderator of social behaviors. However, it remains unknown how LHb regulates social interaction. Here, we show that the hydroxymethylase Tet2 is highly expressed in the LHb. Tet2 conditional knockout (cKO) mice exhibit impaired social preference; however, replenishing Tet2 in the LHb rescues social preference impairment in Tet2 cKO mice. Tet2 cKO alters DNA hydroxymethylation (5hmC) modifications in genes that are related to neuronal functions, as is confirmed by miniature two-photon microscopy data. Further, Tet2 knockdown in the glutamatergic neurons of LHb causes impaired social behaviors, but the inhibition of glutamatergic excitability restores social preference. Mechanistically, we identify that Tet2 deficiency reduces 5hmC modifications on the Sh3rf2 promoter and Sh3rf2 mRNA expression. Interestingly, Sh3rf2 overexpression in the LHb rescues social preference in Tet2 cKO mice. Therefore, Tet2 in the LHb may be a potential therapeutic target for social behavior deficit-related disorders such as autism.

Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.

Protocol for behavioral tests using chemogenetically manipulated mice

Behavioral analyses using mice chemogenetically manipulated by designer receptors exclusively activated by designer drugs (DREADDs) are powerful tools to elucidate neural functions. Here, we describe the detailed protocols for stereotaxic surgery, adeno-associated virus (AAV)-mediated introduction to Gq-DREADDs in mice, and for behavioral testing and analyses related to anxiety, risk assessment, and burying behaviors. A series of these tests are useful in evaluating animal anxiety and their defensive response patterns to potential threats. For complete details on the use and execution of this protocol, please refer to Horii-Hayashi et al. (2021).

Multidimensional encoding of brain connectomes

The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.