ArticleJournal of neurology2026
Brain imaging reveals hierarchical topology changes and stage-dependent impairments in autoimmune encephalitis.
Article in Journal of neurology, 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
10 authors.
Funding
Abstract
backgroundAutoimmune encephalitis is a rapidly progressing neurological disorder caused by aberrant immune responses against neural antigens. It presents with severe neuropsychiatric symptoms such as seizures and cognitive decline, highlighting the need to clarify its neural mechanisms.
objectivesTo investigate functional and structural brain network alterations across autoimmune encephalitis clinical phases and explore their potential as diagnostic and prognostic biomarkers.
methodsResting-state functional MRI and diffusion tensor imaging were used to analyze brain network topology in 52 patients, including 30 patients who underwent longitudinal follow-up and 32 age- and sex-matched healthy controls. Functional and structural brain networks were constructed using graph-theoretical approaches, and global and local network measures were compared across groups. Machine learning models classified disease status and disease phase.
resultsPatients with autoimmune encephalitis showed significant disruptions in global and local network efficiencies, particularly in the medial occipital and inferior temporal lobes, more pronounced during the acute phase. Classification models achieved high accuracy distinguishing patients from controls (AUC = 0.97 functional, 0.85 structural) and acute from convalescent phases (AUC = 0.98, 0.83).
conclusionsAutoimmune encephalitis involves stage-dependent network impairments reflecting disrupted connectivity. Network efficiency may serve as a biomarker for diagnosis and prognosis, supporting multimodal imaging to guide personalized therapeutic strategies.
Indexed as
Identifiers
41661336What 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.