Analysis of ultrasonic vocalizations from mice using computer vision and machine learning

Fonseca, Antonio H. O.; Santana, Gustavo M.; Ortiz, Gabriela M Bosque; Bampi, Sérgio; Dietrich, Marcelo O

doi:10.7554/elife.59161

Cited by 75 publications

(87 citation statements)

References 40 publications

(71 reference statements)

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“…We compared our proposed detection methodology with several popular USV detection tools, such as MUPET (Van Segbroeck et al, 2017), VocalMat (Fonseca et al, 2021), and DeepSqueak (Coffey et al, 2019). We also wanted to compare our new tool with MSA, which was developed in our lab.…”

Section: Experimental Evaluation Of the Amvoc Detection Methodsmentioning

confidence: 99%

“…To mitigate bias in our evaluation of AMVOC, we also compared AMVOC with the Dataset published alongside VocalMat (hereafter referred to as VM1) by Fonseca et al (2021). VM1 consists of 7 different recordings from 7 mice (5-15 days old, of both sexes) (Fonseca et al, 2021). We did not change AMVOC's pre-determined configuration (parameters and ) for this evaluation, in order to examine how robust the selection of and is for recordings produced in different conditions.…”

Section: Experimental Evaluation Of the Amvoc Detection Methodsmentioning

confidence: 99%

“…As new methods have arisen for the unsupervised assessment of USVs (Van Segbroeck et al, 2017), some have implemented machine learning and neural networks in their processes (Coffey et al, 2019;Fonseca et al, 2021). Machine learning methods have been used improving detection accuracy, but performance is limited by how well a network is trained, and it may not always generalize across experiments.…”

Section: Introductionmentioning

confidence: 99%

“…VocalMat is a MATLAB tool (Fonseca et al (2021)), which uses image processing techniques and differential geometry analysis on the spectrogram of a recording to detect vocalization candidates. It can then classify detected USVs into 12 predefined categories (including noise), by using a CNN.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Analysis of Mouse Vocal Communication (AMVOC): A deep, unsupervised method for rapid detection, analysis, and classification of ultrasonic vocalizations

Stoumpou

Vargas

Schade

et al. 2021

Preprint

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Some aspects of the neural mechanisms underlying mouse ultrasonic vocalizations (USVs) are a useful model for the neurobiology of human speech and speech-related disorders. Much of the research on vocalizations and USVs is limited to offline methods and supervised classification of USVs, hindering the discovery of new types of vocalizations and the study of real-time free behavior. To address these issues, we developed AMVOC (Analysis of Mouse VOcal Communication) as a free, open-source software to analyze and detect USVs in both online and offline modes. When compared to hand-annotated ground-truth USV data, AMVOC's detection functionality (both offline and online) has high accuracy, and outperforms leading methods in noisy conditions, thus allowing for broader experimental use. AMVOC also includes the implementation of an unsupervised deep learning approach that facilitates discovery and analysis of USV data by clustering USVs using latent features extracted by a convolutional autoencoder and isimplemented in a graphical user interface (GUI), also enabling user's evaluation. These results can be used to explore the vocal repertoire space of the analyzed vocalizations. In this way, AMVOC will facilitate vocal analyses in a broader range of experimental conditions and allow users to develop previously inaccessible experimental designs for the study of mouse vocal behavior.

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Section: Experimental Evaluation Of the Amvoc Detection Methodsmentioning

confidence: 99%

Section: Experimental Evaluation Of the Amvoc Detection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis of Mouse Vocal Communication (AMVOC): A deep, unsupervised method for rapid detection, analysis, and classification of ultrasonic vocalizations

Stoumpou

Vargas

Schade

et al. 2021

Preprint

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show abstract

“…While completing the final draft of our present manuscript, a new tool, called 'Vocalmat' (Fonseca et al, 2021) classifier is the AlexNet model (Krizhevsky et al, 2012), which was pre-trained on the ImageNet dataset.…”

Section: Inter-observer Reliability (Ior)mentioning

confidence: 99%

Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)

Abbasi

Balázs²,

Marconi

et al. 2021

Preprint

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House mice communicate through ultrasonic vocalizations (USVs), which are above the range of human hearing (>20 kHz), and several automated methods have been developed for USV detection and classification. Here we evaluate their advantages and disadvantages in a full, systematic comparison. We compared the performance of four detection methods, DeepSqueak (DSQ), MUPET, USVSEG, and the Automatic Mouse Ultrasound Detector (A-MUD). Moreover, we compared these to human-based manual detection (considered as ground truth), and evaluated the inter-observer reliability. All four methods had comparable rates of detection failure, though A-MUD outperformed the others in terms of true positive rates for recordings with low or high signal-to-noise ratios. We also did a systematic comparison of existing classification algorithms, where we found the need to develop a new method for automating the classification of USVs using supervised classification, bootstrapping on Gammatone Spectrograms, and Convolutional Neural Networks algorithms with Snapshot ensemble learning (BootSnap). It successfully classified calls into 12 types, including a new class of false positives used for detection refinement. BootSnap provides enhanced performance compared to state-of-the-art tools, it has an improved generalizability, and it is freely available for scientific use.

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Micromotion Derived Fluid Shear Stress Mediates Peri‐Electrode Gliosis through Mechanosensitive Ion Channels

et al. 2023

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The development of bioelectronic neural implant technologies has advanced significantly over the past 5 years, particularly in brain–machine interfaces and electronic medicine. However, neuroelectrode‐based therapies require invasive neurosurgery and can subject neural tissues to micromotion‐induced mechanical shear, leading to chronic inflammation, the formation of a peri‐electrode void and the deposition of reactive glial scar tissue. These structures act as physical barriers, hindering electrical signal propagation and reducing neural implant functionality. Although well documented, the mechanisms behind the initiation and progression of these processes are poorly understood. Herein, in silico analysis of micromotion‐induced peri‐electrode void progression and gliosis is described. Subsequently, ventral mesencephalic cells exposed to milliscale fluid shear stress in vitro exhibited increased expression of gliosis‐associated proteins and overexpression of mechanosensitive ion channels PIEZO1 (piezo‐type mechanosensitive ion channel component 1) and TRPA1 (transient receptor potential ankyrin 1), effects further confirmed in vivo in a rat model of peri‐electrode gliosis. Furthermore, in vitro analysis indicates that chemical inhibition/activation of PIEZO1 affects fluid shear stress mediated astrocyte reactivity in a mitochondrial‐dependent manner. Together, the results suggest that mechanosensitive ion channels play a major role in the development of a peri‐electrode void and micromotion‐induced glial scarring at the peri‐electrode region.

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Analysis of ultrasonic vocalizations from mice using computer vision and machine learning

Cited by 75 publications

References 40 publications

Analysis of Mouse Vocal Communication (AMVOC): A deep, unsupervised method for rapid detection, analysis, and classification of ultrasonic vocalizations

Analysis of Mouse Vocal Communication (AMVOC): A deep, unsupervised method for rapid detection, analysis, and classification of ultrasonic vocalizations

Capturing the songs of mice with an improved detection and classification method for ultrasonic vocalizations (BootSnap)

Micromotion Derived Fluid Shear Stress Mediates Peri‐Electrode Gliosis through Mechanosensitive Ion Channels

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