Nicholas J Luciw, Anahit Grigorian, Mikaela K Dimick, Guocheng Jiang, J Jean Chen, Simon J Graham, Benjamin I Goldstein, Bradley J MacIntosh
{"title":"使用脑血流模式对患有双相情感障碍的青年与健康青年进行分类。","authors":"Nicholas J Luciw, Anahit Grigorian, Mikaela K Dimick, Guocheng Jiang, J Jean Chen, Simon J Graham, Benjamin I Goldstein, Bradley J MacIntosh","doi":"10.1503/jpn.230012","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and <i>T</i> <sub>1</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.</p><p><strong>Results: </strong>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 (<i>p</i> = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (Δ<i>CBF</i> = -2.94 mL/100 g/min; permutation test <i>p</i> = 0047). Classification performance decreased when considering rfMRI data.</p><p><strong>Limitations: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":50073,"journal":{"name":"Journal of Psychiatry & Neuroscience","volume":"48 4","pages":"E305-E314"},"PeriodicalIF":4.1000,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bb/bd/48-4-E305.PMC10473037.pdf","citationCount":"0","resultStr":"{\"title\":\"Classifying youth with bipolar disorder versus healthy youth using cerebral blood flow patterns.\",\"authors\":\"Nicholas J Luciw, Anahit Grigorian, Mikaela K Dimick, Guocheng Jiang, J Jean Chen, Simon J Graham, Benjamin I Goldstein, Bradley J MacIntosh\",\"doi\":\"10.1503/jpn.230012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and <i>T</i> <sub>1</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.</p><p><strong>Results: </strong>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 (<i>p</i> = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (Δ<i>CBF</i> = -2.94 mL/100 g/min; permutation test <i>p</i> = 0047). Classification performance decreased when considering rfMRI data.</p><p><strong>Limitations: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":50073,\"journal\":{\"name\":\"Journal of Psychiatry & Neuroscience\",\"volume\":\"48 4\",\"pages\":\"E305-E314\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2023-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bb/bd/48-4-E305.PMC10473037.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Psychiatry & Neuroscience\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1503/jpn.230012\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/7/1 0:00:00\",\"PubModel\":\"Print\",\"JCR\":\"Q2\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Psychiatry & Neuroscience","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1503/jpn.230012","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/7/1 0:00:00","PubModel":"Print","JCR":"Q2","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
Classifying youth with bipolar disorder versus healthy youth using cerebral blood flow patterns.
Background: 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.
Methods: Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and T1-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.
Results: 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 (ΔCBF = -2.94 mL/100 g/min; permutation test p = 0047). Classification performance decreased when considering rfMRI data.
Limitations: 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.
Conclusion: 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.
期刊介绍：
The Journal of Psychiatry & Neuroscience publishes papers at the intersection of psychiatry and neuroscience that advance our understanding of the neural mechanisms involved in the etiology and treatment of psychiatric disorders. This includes studies on patients with psychiatric disorders, healthy humans, and experimental animals as well as studies in vitro. Original research articles, including clinical trials with a mechanistic component, and review papers will be considered.
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