ArticleNature communications2021
MesoNet allows automated scaling and segmentation of mouse mesoscale cortical maps using machine learning.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Energy paradox in REM sleep: balancing supply and consumption in brain metabolism.Communications biology · 2026Article
- A multimodal approach for visualizing and identifying electrophysiological cell types in vivo.Nature communications · 2026Article
- The protocol for mesoscopic wide-field optical imaging in mice: from zero to hero.Biology methods & protocols · 2026Review
- Circuit-level dynamics and propagation of slow wave activity modulate their interplay during the awakening process.iScience · 2025Article
- Multimodal dataset linking wide-field calcium imaging to behavior changes in operant lever-pull task in mice.Scientific data · 2025Article
- Characterization of iGABASnFR2 forNeurophotonics · 2025Article
- Bridging the gap: umIT makes complex imaging data accessible to scientists of all backgrounds.Neurophotonics · 2025Article
- Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.Brain informatics · 2024Review
- A deep learning classification task for brain navigation in rodents using micro-Doppler ultrasound imaging.Heliyon · 2024Article
- Traumatic brain injury disrupts state-dependent functional cortical connectivity in a mouse model.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Article
- To be and not to be: wide-field Ca2+ imaging reveals neocortical functional segmentation combines stability and flexibility.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- High-throughput image analysis with deep learning captures heterogeneity and spatial relationships after kidney injury.Scientific reports · 2023Article
- Peripersonal encoding of forelimb proprioception in the mouse somatosensory cortex.Nature communications · 2023Article
- High fidelity sensory-evoked responses in neocortex after intravenous injection of genetically encoded calcium sensors.bioRxiv : the preprint server for biology · 2023Article
- High fidelity sensory-evoked responses in neocortex after intravenous injection of genetically encoded calcium sensors.Frontiers in neuroscience · 2023Article
- Chronic multiscale resolution of mouse brain networks using combined mesoscale cortical imaging and subcortical fiber photometry.Neurophotonics · 2023Article
- Article
- Functional network properties derived from wide-field calcium imaging differ with wakefulness and across cell type.NeuroImage · 2022Article
- REM sleep is associated with distinct global cortical dynamics and controlled by occipital cortex.Nature communications · 2022Article
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Authors and funding
4 authors.
Funding
Abstract
Understanding the basis of brain function requires knowledge of cortical operations over wide spatial scales and the quantitative analysis of brain activity in well-defined brain regions. Matching an anatomical atlas to brain functional data requires substantial labor and expertise. Here, we developed an automated machine learning-based registration and segmentation approach for quantitative analysis of mouse mesoscale cortical images. A deep learning model identifies nine cortical landmarks using only a single raw fluorescent image. Another fully convolutional network was adapted to delimit brain boundaries. This anatomical alignment approach was extended by adding three functional alignment approaches that use sensory maps or spatial-temporal activity motifs. We present this methodology as MesoNet, a robust and user-friendly analysis pipeline using pre-trained models to segment brain regions as defined in the Allen Mouse Brain Atlas. This Python-based toolbox can also be combined with existing methods to facilitate high-throughput data analysis.
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