ReviewNature reviews. Neuroscience2026
Joint modelling of brain and behaviour dynamics with artificial intelligence.
Review in Nature reviews. Neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Ecological Visual Processing in the Mouse.Annual review of neuroscience · 2026Review
- A Deep Learning Framework for Spatiotemporal Modeling of Visual Task fMRI.bioRxiv : the preprint server for biology · 2026Article
- Multiple subject tracking in long-term, behavioral studies of rodents.Frontiers in molecular neuroscience · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence has created tremendous advances for many scientific and engineering applications. In this Review, we synthesize recent advances in joint brain-behaviour modelling of neural and behavioural data, with a focus on methodological innovations, scientific and technical motivations, and key areas for future innovation. We discuss how these tools reveal the shared structure between the brain and behaviour and how they can be used for both science and engineering aims. We highlight how three broad classes with differing aims - discriminative, generative and contrastive - are shaping joint modelling approaches. We also discuss recent advances in behavioural analysis approaches, including pose estimation, hierarchical behaviour analysis and multimodal-language models, which could influence the next generation of joint models. Finally, we argue that considering not only the performance of models but also their trustworthiness and interpretability metrics can help to advance the development of joint modelling approaches.
Indexed as
Identifiers
41339709What Socratic 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 Socratic graph.