LFP publications

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p -values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

Inhibitory G proteins (GNAI/Gα i ) bind to the scaffold G protein signaling modulator 2 (GPSM2) to form a conserved polarity complex that regulates cytoskeleton organization. GPSM2 keeps GNAI in a guanosine diphosphate (GDP)-bound state, but how GPSM2-GNAI is generated or relates to heterotrimeric G protein signaling remains unclear. We find that RGS12, a GTPase-activating protein (GAP), is required to polarize GPSM2-GNAI at the hair cell apical membrane and to organize mechanosensory stereocilia in rows of graded heights. Accordingly, RGS12 and the guanine nucleotide exchange factor (GEF) DAPLE are asymmetrically co-enriched at the hair cell apical junction, and Rgs12 mouse mutants are deaf. GPSM2 and RGS12 share GoLoco motifs that stabilize GNAI(GDP), and GPSM2 outcompetes RGS12 to bind GNAI. Our results suggest that polarized GEF/GAP junctional activity might dissociate heterotrimeric G proteins, generating free GNAI(GDP) for GPSM2 at the adjacent apical membrane. GPSM2-GNAI(GDP), in turn, imparts asymmetry to the forming stereocilia to enable sensory function in hair cells.

Hair cells detect sound, head position or water movements when their mechanosensory hair bundle is deflected. Each hair bundle has an asymmetric architecture that restricts stimulus detection to a single axis. Coordinated hair cell orientations within sensory epithelia further tune stimulus detection at the organ level. Here, we identify GPR156, an orphan GPCR of unknown function, as a critical regulator of hair cell orientation. We demonstrate that the transcription factor EMX2 polarizes GPR156 distribution, enabling it to signal through Gαi and trigger a 180° reversal in hair cell orientation. GPR156-Gαi mediated reversal is essential to establish hair cells with mirror-image orientations in mouse otolith organs in the vestibular system and in zebrafish lateral line. Remarkably, GPR156-Gαi also instructs hair cell reversal in the auditory epithelium, despite a lack of mirror-image organization. Overall, our work demonstrates that conserved GPR156-Gαi signaling is integral to the framework that builds directional responses into mechanosensory epithelia.

Background Magnetoencephalography (MEG) has become an increasingly popular technique for non-invasively characterizing neuromagnetic field changes in the brain at a high temporal resolution. To examine the reliability of the MEG signal, we compared magnetic and electrophysiological responses to complex natural stimuli from the same animals. We examined changes in neuromagnetic fields, local field potentials (LFP) and multi-unit activity (MUA) in macaque monkey primary somatosensory cortex that were induced by varying the rate of mechanical stimulation. Stimuli were applied to the fingertips with three inter-stimulus intervals (ISIs): 0.33s, 1s and 2s. Results Signal intensity was inversely related to the rate of stimulation, but to different degrees for each measurement method. The decrease in response at higher stimulation rates was significantly greater for MUA than LFP and MEG data, while no significant difference was observed between LFP and MEG recordings. Furthermore, response latency was the shortest for MUA and the longest for MEG data. Conclusion The MEG signal is an accurate representation of electrophysiological responses to complex natural stimuli. Further, the intensity and latency of the MEG signal were better correlated with the LFP than MUA data suggesting that the MEG signal reflects primarily synaptic currents rather than spiking activity. These differences in latency could be attributed to differences in the extent of spatial summation and/or differential laminar sensitivity.