Unsupervised identification of states from voltage recordings of neural networks

Molecular dynamics analysis of the structural properties of the transglutaminases of Kutzneria albida and Streptomyces mobaraensis

The microbial transglutaminase (TGase) from Streptomyces mobaraensis (MTGase) is widely used for industrial applications. However, in the last decades, TGases from other bacteria have been described. We focused our attention on TGase, from Kutzneria albida (KalbTGase), recently characterized as more selective than MTGase and proposed for applications in drug delivery. By comparison of the crystallographic structures, the volume of the catalytic site results smaller in KalbTGase. We compared KalbTGase and MTGase structural flexibility by molecular dynamics (MD) simulations at different conditions. KalbTGase is more rigid than MTGase at 300 K, but the catalytic site has a preserved conformation in both structures. Preliminary studies at higher temperatures suggest that KalbTGase acquires enhanced conformational flexibility far from the active site region. The volume of the catalytic active site pocket of KalbTGase at room temperature is smaller than that of MTGase, and decreases at 335 K, remaining stable after further temperature increase. On the contrary, in MTGase the pocket volume continues to decrease as the temperature increases. Overall, the results of our study suggest that at room temperature the enhanced specificity of KalbTGase could be related to a more closed catalytic pocket and lower flexibility than MTGase. Moreover, by preliminary results at higher temperature, KalbTGase structural flexibility suggests an adaptability to different substrates not recognized at room temperature. Lower adaptability of MTGase at higher temperature with a reduction of the catalytic pocket, instead, suggests a reduction of its activity.

Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions

The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings.