ReviewTrends in cognitive sciences2026
Dense longitudinal neuroimaging reveals individual brain change trajectories.
Review in Trends in cognitive sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Reconciling global collaboration and data sovereignty in neuroscience.Nature neuroscience · 2026Review
- Practical considerations for dense longitudinal neuroimaging: Ten simple rules for frequent scanning over short time periods.Developmental cognitive neuroscience · 2026Article
- Functional brain organization is stable within individuals across years.bioRxiv : the preprint server for biology · 2026Article
- Literacy acquisition and functional connectivity drive the emergence and continued development of the VWFA.bioRxiv : the preprint server for biology · 2026Article
- Short-Term Developmental Trajectories of Dorsal-Ventral Pathways and Their Relationships with First-Grade Learning.bioRxiv : the preprint server for biology · 2026Article
- Reframing brain aging: neuroinflammation as an interconnected network process.Frontiers in aging · 2026Review
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
- The cellular choreography of brain aging: a neuroimmune network perspective.Frontiers in aging neuroscience · 2026Review
- Bioinformed idiographic symptom networks to identify neurobiological predictors of affective-state transitions in bipolar disorder.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
6 authors.
Funding
Abstract
Longitudinal measurements of brain structure and function are critical for understanding how humans change over time. Traditional longitudinal approaches sample sparsely across large windows of time to estimate coarse, long-term brain changes. This review showcases insights from dense longitudinal neuroimaging (DLN), an emerging approach that samples densely across relatively short windows of time to precisely estimate individual trajectories of brain change. DLN measures multiple samples from individuals throughout critical periods of rapid change. It allows precise estimates of nonlinear trajectories to advance a mechanistic understanding of brain change. Novel findings from this approach are improving our understanding of human cognition, such as the role of the motor system in visual development and learning.
Indexed as
Identifiers
What 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.