When Acetylcholine Unlocks Feedback Inhibition in Cortex

A Real-Time Sensing System for Monitoring Neural Network Degeneration in an Alzheimer’s Disease-on-a-Chip Model

Stem cell-based in vitro models may provide potential therapeutic strategies and allow drug screening for neurodegenerative diseases, including Alzheimer’s disease (AD). Herein, we develop a neural stem cell (NSC) spheroid-based biochip that is characterized by a brain-like structure, well-defined neural differentiation, and neural network formation, representing a brain-on-a-chip. This system consisted of microelectrode arrays with a multichannel platform and allowed the real-time monitoring of network formation and degeneration by impedance analysis. The parameters of this platform for the real-time tracking of network development and organization were established based on our previous study. Subsequently, β-amyloid (Aβ) was added into the brain-on-a-chip system to generate an AD-on-a-chip model, and toxic effects on neurons and the degeneration of synapses were observed. The AD-on-a-chip model may help us to investigate the neurotoxicity of Aβ on neurons and neural networks in real time. Aβ causes neural damage and accumulates around neurites or inside neurospheroids, as observed by immunostaining and scanning electron microscopy (SEM). After incubation with Aβ, reactive oxygen species (ROS) increased, synapse function decreased, and the neurotransmitter-acetylcholine (ACh) concentration decreased were observed. Most importantly, the real-time analysis system monitored the impedance value variation in the system with Aβ incubation, providing consecutive network disconnection data that are consistent with biological data. This platform provides simple, real-time, and convenient sensing to monitor the network microenvironment. The proposed AD-on-a-chip model enhances the understanding of neurological pathology, and the development of this model provides an alternative for the study of drug discovery and cell–protein interactions in the brain.

Aberrant maturation and connectivity of prefrontal cortex in schizophrenia-contribution of NMDA receptor development and hypofunction

The neurobiology of schizophrenia involves multiple facets of pathophysiology, ranging from its genetic basis over changes in neurochemistry and neurophysiology, to the systemic level of neural circuits. Although the precise mechanisms associated with the neuropathophysiology remain elusive, one essential aspect is the aberrant maturation and connectivity of the prefrontal cortex that leads to complex symptoms in various stages of the disease. Here, we focus on how early developmental dysfunction, especially N-methyl-D-aspartate receptor (NMDAR) development and hypofunction, may lead to the dysfunction of both local circuitry within the prefrontal cortex and its long-range connectivity. More specifically, we will focus on an "all roads lead to Rome" hypothesis, i.e., how NMDAR hypofunction during development acts as a convergence point and leads to local gamma-aminobutyric acid (GABA) deficits and input-output dysconnectivity in the prefrontal cortex, which eventually induce cognitive and social deficits. Many outstanding questions and hypothetical mechanisms are listed for future investigations of this intriguing hypothesis that may lead to a better understanding of the aberrant maturation and connectivity associated with the prefrontal cortex.

Interneuron Types as Attractors and Controllers

Cortical interneurons display striking differences in shape, physiology, and other attributes, challenging us to appropriately classify them. We previously suggested that interneuron types should be defined by their role in cortical processing. Here, we revisit the question of how to codify their diversity based upon their division of labor and function as controllers of cortical information flow. We suggest that developmental trajectories provide a guide for appreciating interneuron diversity and argue that subtype identity is generated using a configurational (rather than combinatorial) code of transcription factors that produce attractor states in the underlying gene regulatory network. We present our updated three-stage model for interneuron specification: an initial cardinal step, allocating interneurons into a few major classes, followed by definitive refinement, creating subclasses upon settling within the cortex, and lastly, state determination, reflecting the incorporation of interneurons into functional circuit ensembles. We close by discussing findings indicating that major interneuron classes are both evolutionarily ancient and conserved. We propose that the complexity of cortical circuits is generated by phylogenetically old interneuron types, complemented by an evolutionary increase in principal neuron diversity. This suggests that a natural neurobiological definition of interneuron types might be derived from a match between their developmental origin and computational function.