Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
The link between comprehensive behavioral measurements during a behavioral task and brain-wide neuronal activity is an essential strategy to better understand the brain dynamics underlying the emergence of behavior changes. To tackle this, we provide an extensive, multimodal dataset that includes 15 sessions spanning 2 weeks of motor skill learning, in which 25 mice were trained to pull a lever to obtain water rewards. Simultaneous high-speed videography captured body, facial, and eye movements, and environmental parameters were monitored. The dataset also features resting-state cortical activity and sensory-evoked responses, enhancing its utility for both learning-related and sensory-driven neural dynamics studies. Data are formatted in accordance with the Neurodata Without Borders (NWB) standard, ensuring compatibility with existing analysis tools and adherence to the FAIR principles (Findable, Accessible, Interoperable, Reusable). This resource enables in-depth investigations into the neural mechanisms underlying behavior and learning. The platform encourages collaborative research, supporting the exploration of rapid within-session learning effects, long-term behavioral adaptations, and neural circuit dynamics.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307678 | PMC |
| http://dx.doi.org/10.1038/s41597-025-05482-y | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Hybrid Convolutional Neural Network and Generative Adversarial Network Framework for Robust Deepfake Detection: Advancing Accuracy, Scalability, and Multimodal Integration.
J Vis Exp
August 2025
Department of Computer Science and Engineering, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India.
Deepfakes pose critical threats to digital media integrity and societal trust. This paper presents a hybrid deepfake detection framework combining Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) to address challenges in scalability, generalizability, and adversarial robustness. The framework integrates adversarial training, a temporal decay analysis model, and multimodal detection across audio, video, and text domains.
View Article and Find Full Text PDFEnhancing the Interpretation of Skin Lesion Diagnosis: Concept Adaptive Fine-Tuning of Vision-Language Models.
IEEE J Biomed Health Inform
September 2025
Significant progress has been made in applying deep learning for the automatic diagnosis of skin lesions. However, most models remain unexplainable, which severely hinders their application in clinical settings. Concept-based ante-hoc interpretable models have the potential to clarify the decision-making process of diagnosis by learning high-level, human-understandable concepts, while they can only provide numerical values of conceptual contributions.
View Article and Find Full Text PDFMKD-Net: A multimodal multi-domain knowledge-driven framework for Parkinson's disease diagnosis.
J Xray Sci Technol
September 2025
Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine, Qingdao, China.
Parkinson's disease (PD) is a challenging neurodegenerative condition often prone to diagnostic errors, where early and accurate diagnosis is critical for effective clinical management. However, existing diagnostic methods often fail to fully exploit multimodal data or systematically incorporate expert domain knowledge. To address these limitations, we propose MKD-Net, a multimodal and knowledge-driven diagnostic framework that integrates imaging and non-imaging clinical data with structured expert insights to enhance diagnostic performance.
View Article and Find Full Text PDFA multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.
J Pharm Anal
August 2025
College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
P-glycoprotein (P-gp) is a transmembrane protein widely involved in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs within the human body. Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment. However, existing models rely on limited molecular information, leading to suboptimal model performance for predicting P-gp inhibitors and substrates.
View Article and Find Full Text PDFA comparative study of EEG functional and effective connectivity patterns in children with learning difficulties during reading and math tasks.
Front Neurosci
August 2025
School of Psychology, Hainan Normal University, Haikou, China.
Introduction: This study utilized electroencephalography (EEG) to compare brain functional and effective connectivity patterns in children with reading difficulties (RD) and math difficulties (MD) during specific tasks. The aim was to identify neurophysiological distinctions between these two learning disorders, which often exhibit high comorbidity.
Methods: Data from a publicly available dataset of 28 children (11 RD, 17 MD) aged 7-13 years were analyzed.