Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice DOI Open Access
Evan D. Vickers, David A. McCormick

Published: March 28, 2024

The flow of neural activity across the neocortex during active sensory discrimination is constrained by task-specific cognitive demands, movements, and internal states. During behavior, brain appears to sample from a broad repertoire activation motifs. Understanding how these patterns local global are selected in relation both spontaneous task-dependent behavior requires in-depth study densely sampled at single neuron resolution large regions cortex. In significant advance toward this goal, we developed procedures record mesoscale 2-photon Ca 2+ imaging data two novel vivo preparations that, between them, allow simultaneous access nearly all mouse dorsal lateral neocortex. As proof principle, aligned with behavioral primitives high-level motifs reveal existence populations neurons that coordinated their cortical areas changes movement and/or arousal. methods detail here facilitate identification exploration widespread, spatially heterogeneous ensembles whose related diverse aspects behavior.

Language: Английский

Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data DOI Creative Commons
Grace C. McKenzie‐Smith, Scott Wolf, Julien F. Ayroles

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(2), P. e1012753 - e1012753

Published: Feb. 3, 2025

Animal behavior spans many timescales, from short, seconds-scale actions to daily rhythms over hours life-long changes during aging. To access longer timescales of behavior, we continuously recorded individual Drosophila melanogaster at 100 frames per second for up 7 days a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP produce full-body postural dataset 47 individuals resulting nearly 2 billion pose instances. identify stereotyped behaviors such as grooming, proboscis extension, and locomotion ethograms explore how flies' varies across day experiment. find distinct patterns all behaviors, adding specific information about trends different grooming modalities, extension duration, speed what is known D. circadian cycle. Using our holistic measurements that hour after dawn unique point pattern behavioral composition this tracks well with other indicators health fraction spend moving vs. resting. The method, data, analysis presented here give us new clearer picture revealing novel features hint unexplored underlying biological mechanisms.

Language: Английский

Citations

2
SuperAnimal pretrained pose estimation models for behavioral analysis DOI Creative Commons
Shaokai Ye, Anastasiia Filippova, Jessy Lauer

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 21, 2024

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis first extracting relevant keypoints on animals, known as pose estimation. However, reliable inference poses currently requires domain knowledge and manual labeling effort build supervised models. We present SuperAnimal, a method develop unified foundation models that can be used over 45 species, without additional labels. These show excellent performance across six estimation benchmarks. demonstrate how fine-tune the (if needed) differently labeled data provide tooling unsupervised video adaptation boost decrease jitter frames. If fine-tuned, SuperAnimal are 10-100× more efficient than prior transfer-learning-based approaches. illustrate utility our classification kinematic analysis. Collectively, we data-efficient solution

Language: Английский

Citations

12
Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools DOI
Dan Biderman, Matthew R Whiteway, Cole Hurwitz

et al.

Nature Methods, Journal Year: 2024, Volume and Issue: 21(7), P. 1316 - 1328

Published: June 25, 2024

Language: Английский

Citations

12
Characterizing the structure of mouse behavior using Motion Sequencing DOI
Sherry Lin, Winthrop F. Gillis, Caleb Weinreb

et al.

Nature Protocols, Journal Year: 2024, Volume and Issue: 19(11), P. 3242 - 3291

Published: June 26, 2024

Language: Английский

Citations

9
Elephants and algorithms: a review of the current and future role of AI in elephant monitoring DOI Creative Commons
Leandra Brickson,

Libby Zhang,

Fritz Vollrath

et al.

Journal of The Royal Society Interface, Journal Year: 2023, Volume and Issue: 20(208)

Published: Nov. 1, 2023

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour conservation strategies. Using elephants, a crucial species in Africa Asia’s protected areas, as focal point, we delve into the role AI ML their conservation. Given increasing amounts data gathered from variety sensors like cameras, microphones, geophones, drones satellites, challenge lies managing interpreting this vast data. New techniques offer solutions streamline process, helping us extract vital information that might otherwise be overlooked. This paper focuses on different AI-driven monitoring methods potential for improving elephant Collaborative efforts between experts ecological researchers are essential leveraging these innovative technologies enhanced wildlife conservation, setting precedent numerous other species.

Language: Английский

Citations

19
Advancing social behavioral neuroscience by integrating ethology and comparative psychology methods through machine learning DOI Creative Commons
Joeri Bordes, Lucas Miranda, Bertram Müller‐Myhsok

et al.

Neuroscience & Biobehavioral Reviews, Journal Year: 2023, Volume and Issue: 151, P. 105243 - 105243

Published: May 22, 2023

Social behavior is naturally occurring in vertebrate species, which holds a strong evolutionary component and crucial for the normal development survival of individuals throughout life. Behavioral neuroscience has seen different influential methods social behavioral phenotyping. The ethological research approach extensively investigated natural habitats, while comparative psychology was developed utilizing standardized univariate tests. advanced precise tracking tools, together with post-tracking analysis packages, recently enabled novel phenotyping method, that includes strengths both approaches. implementation such will be beneficial fundamental but also enable an increased understanding influences many factors can influence behavior, as stress exposure. Furthermore, future increase number data modalities, sensory, physiological, neuronal activity data, thereby significantly enhance our biological basis guide intervention strategies abnormalities psychiatric disorders.

Language: Английский

Citations

18
Reproducible and fully automated testing of nocifensive behavior in mice DOI Creative Commons
Christopher Dedek, Mehdi A. Azadgoleh, Steven A. Prescott

et al.

Cell Reports Methods, Journal Year: 2023, Volume and Issue: 3(12), P. 100650 - 100650

Published: Dec. 1, 2023

Language: Английский

Citations

14
Challenges and advanced concepts for the assessment of learning and memory function in mice DOI Creative Commons
Benjamin Lang, Pia Kahnau, Katharina Hohlbaum

et al.

Frontiers in Behavioral Neuroscience, Journal Year: 2023, Volume and Issue: 17

Published: Sept. 21, 2023

The mechanisms underlying the formation and retrieval of memories are still an active area research discussion. Manifold models have been proposed refined over years, with most assuming a dichotomy between memory processes involving non-conscious conscious mechanisms. Despite our incomplete understanding mechanisms, tests learning count among performed behavioral experiments. Here, we will discuss available protocols for testing using example prevalent animal species in research, laboratory mouse. A wide range has developed mice to test, e.g., object recognition, spatial learning, procedural memory, sequential problem solving, operant- fear conditioning, social recognition. Those assays carried out individual subjects apparatuses such as arenas mazes, which allow high degree standardization across laboratories straightforward data interpretation but not without caveats limitations. In there is growing concern about translatability study results welfare, leading novel approaches beyond established protocols. present some more recent developments advanced concepts testing, multi-step lockboxes, groups animals, well home cage-based supported by automated tracking solutions; weight their potential limitations against those paradigms. Shifting focus from classical experimental chamber settings natural rodents comes new set challenges researchers, also offers opportunity understand conclusive way than attainable conventional test We predict embrace increase studies relying on methods higher automatization, naturalistic- setting integrated tasks future. confident these trends suited alleviate burden improve designs research.

Language: Английский

Citations

13
Quantifying social roles in multi-animal videos using subject-aware deep-learning DOI

Kelly Goss,

Lézio Soares Bueno-Júnior, Katherine A. Stangis

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 10, 2024

ABSTRACT Analyzing social behaviors is critical for many fields, including neuroscience, psychology, and ecology. While computational tools have been developed to analyze videos containing animals engaging in limited interactions under specific experimental conditions, automated identification of the roles freely moving individuals a multi-animal group remains unresolved. Here we describe deep-learning-based system – named LabGym2 identifying quantifying groups. This uses subject-aware approach: it evaluates behavioral state every individual two or more while factoring its environmental surroundings. We demonstrate performance deep-learning different species assays, from partner preference freely-moving insects primate field. Our deep learning approach provides controllable, interpretable, efficient framework enable new paradigms systematic evaluation interactive behavior identified within group.

Language: Английский

Citations

5
Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools DOI Creative Commons
Dan Biderman, Matthew R Whiteway, Cole Hurwitz

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: April 28, 2023

Abstract Contemporary pose estimation methods enable precise measurements of behavior via supervised deep learning with hand-labeled video frames. Although effective in many cases, the approach requires extensive labeling and often produces outputs that are unreliable for downstream analyses. Here, we introduce “Lightning Pose,” an efficient package three algorithmic contributions. First, addition to training on a few labeled frames, use unlabeled videos penalize network whenever its predictions violate motion continuity, multiple-view geometry, posture plausibility (semi-supervised learning). Second, architecture resolves occlusions by predicting any given frame using surrounding Third, refine post-hoc combining ensembling Kalman smoothing. Together, these components render trajectories more accurate scientifically usable. We release cloud application allows users label data, train networks, predict new directly from browser.

Language: Английский

Citations

12