Role of auditory-somatosensory corticothalamic circuit integration in analgesia.

Our sensory environment is permeated by a diverse array of auditory and somatosensory stimuli. The pairing of acoustic signals with concurrent or forthcoming tactile cues are abundant in everyday life and various survival contexts across species, thus deeming the ability to integrate sensory inputs arising from the combination of these stimuli as crucial. The corticothalamic system plays a critical role in orchestrating the construction, integration and distribution of the information extracted from these sensory modalities. In this mini-review, we provide a circuit-level description of the auditory corticothalamic pathway in conjunction with adjacent corticothalamic somatosensory projections. Although the extent of the functional interactions shared by these pathways is not entirely elucidated, activation of each of these systems appears to modulate sensory perception in the complementary domain. Several specific issues are reviewed. Under certain environmental noise conditions, the spectral information of a sound could induce modulations in nociception and even induce analgesia. We begin by discussing recent findings by Zhou et al. (2022) implicating the corticothalamic system in mediating sound-induced analgesia. Next, we describe relevant components of the corticothalamic pathway's functional organization. Additionally, we describe an emerging body of literature pointing to intrathalamic circuitry being optimal for controlling and selecting sensory signals across modalities, with the thalamic reticular nucleus being a candidate mechanism for directing cross-modal interactions. Finally, Ca2+ bursting in thalamic neurons evoked by the thalamic reticular nucleus is explored.

Universal automated classification of the acoustic startle reflex using machine learning.

The startle reflex (SR), a robust, motor response elicited by an intense auditory, visual, or somatosensory stimulus has been widely used as a tool to assess psychophysiology in humans and animals for almost a century in diverse fields such as schizophrenia, bipolar disorder, hearing loss, and tinnitus. Previously, SR waveforms have been ignored, or assessed with basic statistical techniques and/or simple template matching paradigms. This has led to considerable variability in SR studies from different laboratories, and species. In an effort to standardize SR assessment methods, we developed a machine learning algorithm and workflow to automatically classify SR waveforms in virtually any animal model including mice, rats, guinea pigs, and gerbils obtained with various paradigms and modalities from several laboratories. The universal features common to SR waveforms of various species and paradigms are examined and discussed in the context of each animal model. The procedure describes common results using the SR across species and how to fully implement the open-source R implementation. Since SR is widely used to investigate toxicological or pharmaceutical efficacy, a detailed and universal SR waveform classification protocol should be developed to aid in standardizing SR assessment procedures across different laboratories and species. This machine learning-based method will improve data reliability and translatability between labs that use the startle reflex paradigm.