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1.
Front Pharmacol ; 14: 1240295, 2023.
Article in English | MEDLINE | ID: mdl-37869749

ABSTRACT

There has recently been a resurgence of interest in psychedelic compounds based on studies demonstrating their potential therapeutic applications in treating post-traumatic stress disorder, substance abuse disorders, and treatment-resistant depression. Despite promising efficacy observed in some clinical trials, the full range of biological effects and mechanism(s) of action of these compounds have yet to be fully established. Indeed, most studies to date have focused on assessing the psychological mechanisms of psychedelics, often neglecting the non-psychological modes of action. However, it is important to understand that psychedelics may mediate their therapeutic effects through multi-faceted mechanisms, such as the modulation of brain network activity, neuronal plasticity, neuroendocrine function, glial cell regulation, epigenetic processes, and the gut-brain axis. This review provides a framework supporting the implementation of a multi-faceted approach, incorporating in silico, in vitro and in vivo modeling, to aid in the comprehensive understanding of the physiological effects of psychedelics and their potential for clinical application beyond the treatment of psychiatric disorders. We also provide an overview of the literature supporting the potential utility of psychedelics for the treatment of brain injury (e.g., stroke and traumatic brain injury), neurodegenerative diseases (e.g., Parkinson's and Alzheimer's diseases), and gut-brain axis dysfunction associated with psychiatric disorders (e.g., generalized anxiety disorder and major depressive disorder). To move the field forward, we outline advantageous experimental frameworks to explore these and other novel applications for psychedelics.

2.
Front Neurosci ; 17: 1171115, 2023.
Article in English | MEDLINE | ID: mdl-37397454

ABSTRACT

Hippocampal neural networks are distinctly capable of integrating multi-modal sensory inputs to drive memory formation. Neuroscientific investigations using simplified in vitro models have greatly relied on planar (2D) neuronal cultures made from dissociated tissue. While these models have served as simple, cost-effective, and high-throughput tools for examining various morphological and electrophysiological characteristics of hippocampal networks, 2D cultures fail to reconstitute critical elements of the brain microenvironment that may be necessary for the emergence of sophisticated integrative network properties. To address this, we utilized a forced aggregation technique to generate high-density (>100,000 cells/mm3) multi-cellular three-dimensional aggregates using rodent embryonic hippocampal tissue. We contrasted the emergent structural and functional properties of aggregated (3D) and dissociated (2D) cultures over 28 days in vitro (DIV). Hippocampal aggregates displayed robust axonal fasciculation across large distances and significant neuronal polarization, i.e., spatial segregation of dendrites and axons, at earlier time points compared to dissociated cultures. Moreover, we found that astrocytes in aggregate cultures self-organized into non-overlapping quasi-domains and developed highly stellate morphologies resembling astrocyte structures in vivo. We maintained cultures on multi-electrode arrays (MEAs) to assess spontaneous electrophysiological activity for up to 28 DIV. We found that 3D networks of aggregated cultures developed highly synchronized networks and with high burstiness by 28 DIV. We also demonstrated that dual-aggregate networks became active by 7 DIV, in contrast to single-aggregate networks which became active and developed synchronous bursting activity with repeating motifs by 14 DIV. Taken together, our findings demonstrate that the high-density, multi-cellular, 3D microenvironment of hippocampal aggregates supports the recapitulation of emergent biofidelic morphological and functional properties. Our findings suggest that neural aggregates may be used as segregated, modular building blocks for the development of complex, multi-nodal neural network topologies.

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