Classifying Youth with Bipolar Disorder Versus Healthy Youth Using Cerebral Blood Flow Patterns

Overview

Journal J Psychiatry Neurosci

Specialty Psychiatry

Date 2023 Aug 29

PMID 37643801

Authors

Nicholas J Luciw

Anahit Grigorian

Mikaela K Dimick

Guocheng Jiang

J Jean Chen

Simon J Graham

Benjamin I Goldstein

Bradley J MacIntosh

Affiliations

Soon will be listed here.

Abstract

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 -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 ( = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (Δ = -2.94 mL/100 g/min; permutation test = 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.

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