TY - JOUR T1 - Unsupervised methods for detection of neural states: case study of hippocampal-amygdala interactions JF - eneuro JO - eNeuro DO - 10.1523/ENEURO.0484-20.2021 SP - ENEURO.0484-20.2021 AU - Francesco Cocina AU - Andreas Vitalis AU - Amedeo Caflisch Y1 - 2021/09/20 UR - http://www.eneuro.org/content/early/2021/09/22/ENEURO.0484-20.2021.abstract N2 - The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because inter-areal 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 data set 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, 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, internal 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 2 of the 3 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, multi-site recordings.Significance statementWe develop an analysis pipeline for neuroscience data sets. We test it on a published example of multi-electrode recordings of rats in a range of behaviors: running on a track, sleeping, collecting rewards, etc. We adopt nonlinear analysis techniques that are able to quantify directed interactions between different signals, here oscillations of two brain regions in different frequency bands. Using the entire recordings and, importantly, distinguishing each animal, we provide a high-resolution overview of the functional interplay of the two regions. Putative neural states that the animals can be in are derived from a time-aware clustering of the large data sets. When discriminating experimental annotations like run speed, we provide evidence that our methodology outperforms common clustering techniques. ER -