ArticleCommunications biology2024
Heterogenous brain activations across individuals localize to a common network.
Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of neural correlates of working memory, reward, and emotion processing in major depressive disorder using AES-SDM.BMC psychiatry · 2026Pooled it
- A view-engage-predict framework for enhancing brain-behavior mapping with naturalistic movie-watching fMRI.Communications biology · 2026Article
- Investigating the methodological foundation of lesion network mapping.Nature neuroscience · 2026Article
- Sensory network dysregulation in type 2 diabetes: Linking olfactory, visual, and cognitive function.Diabetes, obesity & metabolism · 2026Article
- Network localization of functional and structural correlates of apathy in Parkinson's disease.Frontiers in systems neuroscience · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.
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