Functional connectivity between Layer 2/3 and Layer 5 neurons in prefrontal cortex of nonhuman primates during a delayed match-to-sample task

Ketamine normalizes binge drinking-induced defects in glutamatergic synaptic transmission and ethanol drinking behavior in female but not male mice

Ketamine is a fast acting experimental antidepressant with significant therapeutic potential for emotional disorders such as major depressive disorder and alcohol use disorders. Of particular interest is binge alcohol use, which during intermittent withdrawal from drinking involves depressive-like symptoms reminiscent of major depressive disorder. Binge drinking has been successfully modeled in mice with the Drinking in the Dark (DID) paradigm, which involves daily access to 20% ethanol, for a limited duration and selectively during the dark phase of the circadian light cycle. Here we demonstrate that DID exposure reduces the cell surface expression of NMDA- and AMPA-type glutamate receptors in the prelimbic cortex (PLC) of female but not male mice, along with reduced activity of the mammalian target of rapamycin (mTOR) signaling pathway. Pretreatment with an acute subanesthetic dose of ketamine suppresses binge-like ethanol consumption in female but not male mice. Lastly, DID-exposure reduces spontaneous glutamatergic synaptic transmission in the PLC of both sexes, but synaptic transmission is rescued by ketamine selectively in female mice. Thus, ketamine may have therapeutic potential as an ethanol binge suppressing agent selectively in female subjects.

Sparse Large-Scale Nonlinear Dynamical Modeling of Human Hippocampus for Memory Prostheses

In order to build hippocampal prostheses for restoring memory functions, we build sparse multi-input, multi-output (MIMO) nonlinear dynamical models of the human hippocampus. Spike trains are recorded from hippocampal CA3 and CA1 regions of epileptic patients performing a variety of memory-dependent delayed match-to-sample (DMS) tasks. Using CA3 and CA1 spike trains as inputs and outputs respectively, sparse generalized Laguerre-Volterra models are estimated with group lasso and local coordinate descent methods to capture the nonlinear dynamics underlying the CA3-CA1 spike train transformations. These models can accurately predict the CA1 spike trains based on the ongoing CA3 spike trains during multiple memory events, e.g., sample presentation, sample response, match presentation and match response, of the DMS task, and thus will serve as the computational basis of human hippocampal memory prostheses.

Low circulating levels of bisphenol-A induce cognitive deficits and loss of asymmetric spine synapses in dorsolateral prefrontal cortex and hippocampus of adult male monkeys

Bisphenol-A (BPA) is widely used in the manufacture of plastics, epoxy resins, and certain paper products. A majority of the population in the developed world is routinely exposed to BPA from multiple sources and has significant circulating levels of BPA. Although BPA is categorized as an endocrine disruptor with a growing literature on adverse effects, it is uncertain whether cognitive dysfunction is induced in humans by exposure to BPA. The present study examined the impact of BPA in primate brain by exposing adult male vervet monkeys for 4 weeks continuously to circulating levels of BPA that were in the range measured in studies of humans environmentally exposed to BPA. This regimen of exposure to BPA decreased both working memory accuracy and the number of excitatory synaptic inputs on dendritic spines of pyramidal neurons in two brain regions that are necessary for working memory (prefrontal cortex and hippocampus). These observed behavioral and synaptic effects were ameliorated following withdrawal from BPA. As Old World monkeys (e.g., vervets) and humans share some uniquely primate morphological, endocrine, and cognitive traits, this study indicates the potential for significant cognitive disruption following exposure of humans to BPA.

Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling

This paper presents a systems identification approach for studying the long-term synaptic plasticity using natural spiking activities. This approach consists of three modeling steps. First, a multi-input, single-output (MISO), nonlinear dynamical spiking neuron model is formulated to estimate and represent the synaptic strength in means of functional connectivity between input and output neurons. Second, this MISO model is extended to a nonstationary form to track the time-varying properties of the synaptic strength. Finally, a Volterra modeling method is used to extract the synaptic learning rule, e.g., spike-timing-dependent plasticity, for the explanation of the input-output nonstationarity as the consequence of the past input-output spiking patterns. This framework is developed to study the underlying mechanisms of learning and memory formation in behaving animals, and may serve as the computational basis for building the next-generation adaptive cortical prostheses.