Biology subjects

Eisenreich, B. R.

Publications and source records attributed to Eisenreich, B. R..

3 recordsLinked to original sources

bioRxiv2025

Optimal parameters for measuring multiband auditory brainstem responses to continuous speech

Accurate clinical hearing assessment depends on efficient, engaging measures designed to evaluate ecologically relevant stimuli. Often brief tones or narrowband noise stimuli are used, providing a useful but limited snapshot of hearing function. Including dynamic speech offers a means to capture how the hearing system encodes complex sounds critical for everyday communication. Here we describe the optimal parameters for using audiobook continuous speech with the multiband peaky speech paradigm to measure frequency-specific auditory brainstem responses (ABRs) to standard audiological octave bands from 500-8000 Hz in each ear simultaneously. Using computational modeling and direct human ABR testing in adults with normal hearing, we demonstrate that continuous speech signals with a chirp phase profile and fundamental frequency (f0) lowered to the range of 90-110 Hz evoke the largest ABR wave V amplitudes. This amplitude boost occurs when any narrators f0 is lowered to this optimal range, but the largest responses occur for narrators with original f0s below 170 Hz. We also confirmed that different narrator speech stimuli with these optimized parameters can evoke similarly sized ABRs, but some minor differences remain for testing time. Ultimately, optimizing phase-f0 parameters substantially sped up the median testing time to obtain robust audiobook-based multiband ABRs to within 14 minutes, thereby making this paradigm more feasible for future research and clinical translation.

neuroscience↗

bioRxiv2021

Hierarchical organization of rhesus macaque behavior

Primatologists, psychologists and neuroscientists have long hypothesized that primate behavior is highly structured. However, fully delineating that structure has been impossible due to the difficulties of precision behavioral tracking. Here we analyzed a dataset consisting of continuous measures of the 3D position of fifteen body landmarks from two male rhesus macaques (Macaca mulatta) performing three different tasks in a large unrestrained environment over many hours. Using an unsupervised embedding approach on the tracked joints, we identified commonly repeated pose patterns, which we call postures. We found that macaques behavior is characterized by 49 distinct identifiable postures, lasting an average of 0.6 seconds each. We found evidence that behavior is hierarchically organized, in that transitions between poses tend to occur within larger modules, which correspond to intuitively identifiably actions; these actions are in turn organized hierarchically. Our behavioral decomposition allows us to identify universal (cross-individual and cross-task) and unique (specific to each individual and task) principles of behavior. These results demonstrate the hierarchical nature of primate behavior and provide a method for the automated "ethogramming" of primate behavior.

animal behavior and cognition↗

bioRxiv2020

OpenMonkeyStudio: Automated Markerless Pose Estimation in Freely Moving Macaques

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and several fields of medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure its behavior, specifically, its pose (position of multiple major body landmarks) in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a novel deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 precisely calibrated and synchronized machine vision cameras that encircle an open 2.45mx2.45mx2.75m enclosure. The resulting multiview image streams allow for novel data augmentation via 3D reconstruction of hand-annotated images that in turn train a robust view-invariant deep neural network model. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track two monkey social interactions without human intervention. We also make the training data (195,228 images) and trained detection model publicly available.

neuroscience↗