ArticleNeuroimage. Reports2026
Harnessing machine learning for functional connectivity-based feature discovery in post-traumatic epilepsy.
Article in Neuroimage. Reports, 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
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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
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Who cites it
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Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
•Leakage-free nested CV-RFE identifies a reproducible connectivity signature that characterizes the PTE-diseased state in our cohort.•Data-driven feature selection is essential for classification performance, with only four stable functional connections (from an original feature space of 4950) able to distinguish PTE from TBI with an AUC of 0.95.•We identify a specific connectivity phenotype in our PTE cohort which includes the Visual network as a hub of PTE-related changes, left-hemisphere overrepresentation and hyper-coupling, and right-hemisphere un-coupling.•The resting-state network disruptions identified by our ML paradigm went unrecognized by direct comparison between groups and traditional statistics, supporting the use of advanced data-driven methodologies for exploring functional connectivity in PTE.
Identifiers
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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.