ReviewNature neuroscience2025
Integrating multimodal data to understand cortical circuit architecture and function.
Review in Nature neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Deep-learning-assisted simulation of a cortical circuit: integrating anatomy, physiology and function.bioRxiv : the preprint server for biology · 2026Article
- Neurodatascience: Past, Present, and Future.Data science in science · 2026Article
- From microfluidics to nanodelivery: artificial intelligence reshapes neuropharmacology research strategies.Frontiers in pharmacology · 2026Review
- Container-based framework for large-scale spiking network simulation.Frontiers in neuroscience · 2026Article
- Grand challenges for systems neuroscience: perspectives and opportunities.Frontiers in systems neuroscience · 2026Article
- A framework to determine active neurons and networks within the mouse brain reveals how brain activity changes over the course of the day.PLoS biology · 2025Article
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Map of spiking activity underlying change detection in the mouse visual system.bioRxiv : the preprint server for biology · 2025Article
- Complicated architecture of cortical microcircuit: a comprehensive review.Anatomical science international · 2025Review
- Computational reconstruction of evolutionary selection in human brain networks.Frontiers in neuroinformatics · 2025Article
- Multi-modal brain data exploration software for imaging transcriptomics: A comparative evaluation and future directions.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
36 authors.
Funding
Abstract
In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.
Indexed as
Identifiers
40128391What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.