{"id":"W4414440574","doi":"10.1038/s44220-025-00503-6","title":"The network-based underpinnings of persisting symptoms after concussion: a multimodal neuroimaging meta-analysis","year":2025,"lang":"en","type":"article","venue":"Nature Mental Health","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Brain Institute; University of Toronto; University Health Network; Health Sciences Centre; Toronto Rehabilitation Institute; Sunnybrook Health Science Centre","funders":"Medical Research Council; Azrieli Foundation; University of Toronto; Canadian Institutes of Health Research; Sunnybrook Research Institute; National Health and Medical Research Council; Fondation Brain Canada","keywords":"Neuroimaging; Functional magnetic resonance imaging; Connectome; Transcranial magnetic stimulation; Functional neuroimaging; Human Connectome Project; Nerve net; Limiting; Salience (neuroscience); Neuromodulation","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007589111,0.0001975771,0.0005546871,0.0001224883,0.001061095,0.00004802223,0.0002409383,0.00007477069,0.0000301527],"category_scores_gemma":[0.001239682,0.0001257093,0.0006102285,0.001445014,0.0002487788,0.00007440671,0.0001594504,0.0005930908,0.000002111047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000187117,"about_ca_system_score_gemma":0.000174699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007772511,"about_ca_topic_score_gemma":0.0001334134,"domain_scores_codex":[0.9977431,0.0004585957,0.000355332,0.0005541191,0.0004863994,0.0004024587],"domain_scores_gemma":[0.9943563,0.004951553,0.0002372748,0.0003273959,0.00006358048,0.00006386099],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.01083144,0.003009543,0.3098707,0.002883888,0.1678706,0.0001878682,0.01072279,0.06586325,0.01922324,0.1008194,0.2549213,0.05379585],"study_design_scores_gemma":[0.01000847,0.002205765,0.2845697,0.0005849518,0.08804104,0.00005492271,0.003150043,0.3583378,0.03163072,0.005344726,0.2129678,0.003104067],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.1662811,0.04854699,0.004508551,0.7672132,0.004543328,0.002963067,0.000354114,0.0004506281,0.005139022],"genre_scores_gemma":[0.954511,0.00001674914,0.0002938597,0.04460714,0.0000436022,0.00004397519,0.00000397984,0.00001049112,0.0004691556],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7882299,"threshold_uncertainty_score":0.8161194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02962681878623317,"score_gpt":0.3303892847385457,"score_spread":0.3007624659523125,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}