{"id":"W2037194090","doi":"10.1523/jneurosci.3555-14.2015","title":"Quantifying the Microvascular Origin of BOLD-fMRI from First Principles with Two-Photon Microscopy and an Oxygen-Sensitive Nanoprobe","year":2015,"lang":"en","type":"article","venue":"Journal of Neuroscience","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":255,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Center for Research Resources; National Institute on Drug Abuse","keywords":"Neuroscience; Neuroimaging; Functional magnetic resonance imaging; Voxel; Blood-oxygen-level dependent; Blood oxygenation; SIGNAL (programming language); Two-photon excitation microscopy; Nuclear magnetic resonance; Psychology; Artificial intelligence; Computer science; Physics; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003376596,0.0004591126,0.0002592536,0.0002323332,0.0002342571,0.0002916483,0.0004482987,0.000738746,0.0004591899],"category_scores_gemma":[0.0007789013,0.0002950716,0.0002251401,0.0001545308,0.0004747538,0.0006620999,0.000479237,0.0008495847,0.000131383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003824893,"about_ca_system_score_gemma":0.0003310617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006793863,"about_ca_topic_score_gemma":0.001351785,"domain_scores_codex":[0.9998997,0.00001842614,0.000003581246,0.00003212892,0.00003333938,0.0000127551],"domain_scores_gemma":[0.9998134,0.0001146978,0.00002922281,0.00001587503,0.00001556779,0.00001122648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005100879,0.00003301475,0.0005337239,0.0001088706,0.00001559694,0.0001448952,0.00003158933,0.008827627,0.9811277,0.003132097,0.0001672699,0.00582654],"study_design_scores_gemma":[0.0000224151,0.0002836288,0.005073425,0.00002489516,0.00003727005,0.0003796379,0.00003733216,0.3437089,0.6406024,0.007953845,0.001820756,0.00005547277],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2808937,0.0007684029,0.7147834,0.00050475,0.00005573729,0.0001039614,0.0001970193,0.0005357614,0.002157239],"genre_scores_gemma":[0.7797608,0.0009680527,0.2176255,0.0002023761,0.00002475184,0.0001740284,0.0001079397,0.00006261556,0.001073965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000738746,"threshold_uncertainty_score":0.002775133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08288084020422577,"score_gpt":0.3617075446834981,"score_spread":0.2788267044792723,"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."}}