PT  - JOURNAL ARTICLE
AU  - Luciw, Nicholas J.
AU  - Grigorian, Anahit
AU  - Dimick, Mikaela K.
AU  - Jiang, Guocheng
AU  - Chen, J. Jean
AU  - Graham, Simon J.
AU  - Goldstein, Benjamin I.
AU  - MacIntosh, Bradley J.
TI  - Classifying youth with bipolar disorder versus healthy youth using cerebral blood flow patterns
AID  - 10.1503/jpn.230012
DP  - 2023 Aug 29
TA  - Journal of Psychiatry and Neuroscience
PG  - E305--E314
VI  - 48
IP  - 4
4099  - http://jpn.ca/content/48/4/E305.short
4100  - http://jpn.ca/content/48/4/E305.full
SO  - JPN2023 Aug 29; 48
AB  - 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.