Citation

Chen J, Zhang X, Qu Y, Peng Y, Song Y, Zhuo C, Zou S, Tian H. Front. Neurosci. 2022; 16: e944585.

Copyright

(Copyright © 2022, Frontiers Research Foundation)

DOI

10.3389/fnins.2022.944585

PMID

36161155

PMCID

PMC9500192

Abstract

Bipolar disorder (BD) is associated with a high risk of suicide. We used proton magnetic resonance spectroscopy ((1)H-MRS) to detect biochemical metabolite ratios in the bilateral prefrontal white matter (PWM) and hippocampus in 32 BD patients with suicidal ideation (SI) and 18 BD patients without SI, identified potential brain biochemical differences and used abnormal metabolite ratios to predict the severity of suicide risk based on the support vector machine (SVM) algorithm. Furthermore, we analyzed the correlations between biochemical metabolites and clinical variables in BD patients with SI. There were three main findings: (1) the highest classification accuracy of 88% and an area under the curve of 0.9 were achieved in distinguishing BD patients with and without SI, with N-acetyl aspartate (NAA)/creatine (Cr), myo-inositol (mI)/Cr values in the bilateral PWM, NAA/Cr and choline (Cho)/Cr values in the left hippocampus, and Cho/Cr values in the right hippocampus being the features contributing the most; (2) the above seven features could be used to predict Self-rating Idea of Suicide Scale scores (r = 0.4261, p = 0.0302); and (3) the level of neuronal function in the left hippocampus may be related to the duration of illness, the level of membrane phospholipid catabolism in the left hippocampus may be related to the severity of depression, and the level of inositol metabolism in the left PWM may be related to the age of onset in BD patients with SI. Our results showed that the combination of multiple brain biochemical metabolites could better predict the risk and severity of suicide in patients with BD and that there was a significant correlation between biochemical metabolic values and clinical variables in BD patients with SI.

Language: en

Keywords

suicidal ideation; machine learning; bipolar disorder; multivariate pattern analysis; proton magnetic resonance spectroscopy; support vector machine