{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:47:19Z","timestamp":1780768039001,"version":"3.54.1"},"reference-count":50,"publisher":"Canadian Science Publishing","issue":"4","license":[{"start":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T00:00:00Z","timestamp":1688428800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.nrcresearchpress.com\/page\/about\/CorporateTextAndDataMining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Psychiatry Neurosci"],"published-print":{"date-parts":[[2023,7,4]]},"abstract":"<jats:sec><jats:title>Background:<\/jats:title><jats:p> Clinical neuroimaging studies often investigate group differences between patients and controls, yet multivariate imaging features may enable individual-level classification. This study aims to classify youth with bipolar disorder (BD) versus healthy youth using grey matter cerebral blood flow (CBF) data analyzed with logistic regressions. <\/jats:p><\/jats:sec><jats:sec><jats:title>Methods:<\/jats:title><jats:p> Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and T<jats:sub>1<\/jats:sub>-weighted images from youth with BD and healthy controls. We used 3 logistic regression models to classify youth with BD versus controls, controlling for age and sex, using mean grey matter CBF as a single explanatory variable, quantitative CBF features based on principal component analysis (PCA) or relative (intensity-normalized) CBF features based on PCA. We also carried out a comparison analysis using rfMRI data. <\/jats:p><\/jats:sec><jats:sec><jats:title>Results:<\/jats:title><jats:p> The study included 46 patients with BD (mean age 17 yr, standard deviation [SD] 1 yr; 25 females) and 49 healthy controls (mean age 16 yr, SD 2 yr; 24 females). Global mean CBF and multivariate quantitative CBF offered similar classification performance that was above chance. The association between CBF images and the feature map was not significantly different between groups ( p = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (\u0394 CBF = \u22122.94 mL\/100 g\/min; permutation test p = 0047). Classification performance decreased when considering rfMRI data. <\/jats:p><\/jats:sec><jats:sec><jats:title>Limitations:<\/jats:title><jats:p> We cannot comment on which CBF principal component is most relevant to the classification. Participants may have had various mood states, comorbidities, demographics and medication records. <\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion:<\/jats:title><jats:p> Brain CBF features can classify youth with BD versus healthy controls with above-chance accuracy using logistic regression. A global CBF feature may offer similar classification performance to distinct multivariate CBF features. <\/jats:p><\/jats:sec>","DOI":"10.1503\/jpn.230012","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T08:15:11Z","timestamp":1693296911000},"page":"E305-E314","source":"Crossref","is-referenced-by-count":1,"title":["Classifying youth with bipolar disorder versus healthy youth using cerebral blood flow patterns"],"prefix":"10.1139","volume":"48","author":[{"given":"Nicholas J.","family":"Luciw","sequence":"first","affiliation":[{"name":"From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont."},{"name":"the Department of Medical Biophysics, University of Toronto, Toronto, Ont."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anahit","family":"Grigorian","sequence":"additional","affiliation":[{"name":"the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mikaela K.","family":"Dimick","sequence":"additional","affiliation":[{"name":"the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont."},{"name":"the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guocheng","family":"Jiang","sequence":"additional","affiliation":[{"name":"From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont."},{"name":"the Department of Medical Biophysics, University of Toronto, Toronto, Ont."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J. Jean","family":"Chen","sequence":"additional","affiliation":[{"name":"the Department of Medical Biophysics, University of Toronto, Toronto, Ont."},{"name":"the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont."},{"name":"the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon J.","family":"Graham","sequence":"additional","affiliation":[{"name":"From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont."},{"name":"the Department of Medical Biophysics, University of Toronto, Toronto, Ont."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin I.","family":"Goldstein","sequence":"additional","affiliation":[{"name":"the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont."},{"name":"the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont."},{"name":"the Department of Psychiatry, 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