A 2026 ADNI study reported 85.23% accuracy for ANA-GNN, a graph neural network that combined structural MRI regional features with clinical variables to classify cognitively normal controls, mild cognitive impairment, and Alzheimer’s disease.1 The result is useful, but the clinical-feature ablation dropped accuracy to 68.35%, so the model should be read as multimodal decision-support research, …
A 2026 serum multiomics study linked occupational noise exposure to tinnitus severity mostly through metabolism: 10 metabolites, including GABA, fumaric acid, and steroid hormone precursors, statistically mediated 92% of the exposure-tinnitus association.1 The result is not a clinical blood test yet, but it pushes tinnitus biology beyond the ear-only frame toward a metabolism-immunity model that …
A 2026 Scientific Reports study of Maryland suicide-death records found that an XGBoost machine-learning model could reach 96.1% positive predictive value in hospital-discharge data at the top 0.1% risk threshold, but it still detected only 46.7% of suicide deaths in that cohort. Research Highlights Precision improved at the narrowest threshold: XGBoost reached PPV 0.961 in …
A 2026 multimodal MRI study found that treatment-resistant depression had lower coordination between brain structure and brain activity than non-treatment-resistant depression in frontal, parietal, motor, and temporal regions. Machine-learning models using those structure-function coupling measures classified treatment-resistant vs. non-treatment-resistant depression with AUC values from 0.886 to 0.950 in the primary atlas analysis.1 Research Highlights TRD …
A 2026 diagnostic-accuracy preprint found that resting-state EEG network features separated non-lesional epilepsy from functional/dissociative seizures (FDS) at 67.5% balanced accuracy, but the signal was not symmetrical: sensitivity was 81.8% for epilepsy and only 53.3% for FDS.1 In plain clinical terms, the model looked more useful as an epilepsy-supporting marker than as a positive test …
A 2026 longitudinal MRI preprint involving 54 people with Friedreich ataxia and 57 controls found 3 biologically interpretable progression subtypes: microstructure-dominant, macrostructure-dominant, and minimal/no progression.1 The clusters make biomarker heterogeneity visible at the research level, while the classifier remains too early to assign individual patients to treatment paths. Research Highlights 3 MRI progression subtypes emerged: …
A 2026 BETA analysis of 199 older adults found that resting-state fMRI could separate 4 anxiety-related tDCS response patterns, but the result was narrower than “brain scan predicts treatment”: only the Robust tDCS Responder subtype showed a statistically reliable Active vs. Sham anxiety advantage across all participants.1 Research Highlights Only 1 subtype cleared the Active …