ArticlePatterns (New York, N.Y.)2026
Automating region selection with genetic algorithms for energy landscape analyses of brain dynamics.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Understanding brain dynamics is essential in cognitive neuroscience. Energy landscape analysis (ELA), which characterizes brain activity using a pairwise maximum entropy model, is a powerful tool for analyzing these dynamics but has traditionally relied on the subjective, manual selection of small regions of interest (ROIs) to satisfy mathematical constraints. We developed ELA/GAopt, a framework that uses genetic algorithms to automate ROI selection from whole-brain atlases in a data-driven manner. ELA/GAopt's consistent identification of reproducible ROI subsets was validated across three independent datasets. In multi-site clinical data, the framework identified and replicated autism-specific dynamics characterized by global co-activation within sensory-motor and visual networks. These results demonstrate that ELA/GAopt provides a systematic, objective approach for characterizing condition-specific brain dynamics, thereby establishing a methodological foundation for future externally validated biomarker studies and the systematic exploration of brain state transitions.
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.