Key result
Multimodality machine learning classifiers incorporating pain avoidance and brain imaging features identified suicide attempters with 63%-92% accuracy, outperforming single-modality classifiers.
Why the study?
Neural circuits underlying pain processing in suicide attempters with major depressive disorder had not been described comprehensively.
Does a multimodality machine learning classifier improve the accuracy of identifying suicide attempters in patients with MDD compared to single-modality classifiers?
Observational
Does a multimodality machine learning classifier improve the accuracy of identifying suicide attempters in patients with MDD compared to single-modality classifiers?
A multimodality machine learning approach combining psychological pain avoidance and brain imaging features accurately identifies suicide attempters among patients with MDD.
May enhance suicide attempter detection in MDD; hypothesis-generating, needs prospective validation.
Pain avoidance can effectively classify suicide attempters from non-attempters among patients with major depressive disorder (MDD). However, the neural circuits underlying pain processing in suicide attempters have not been described comprehensively. In Study 1, we recruited MDD patients with a history of suicide attempts (MDD-SA), and those without (MDD-NSA) to examine the patterns of psychological pain using the latent profile analysis. Further, in Study 2, participants including the MDD-SA, MDD-NSA, and healthy controls underwent resting-state functional magnetic resonance imaging. We used machine learning that included features of gray matter volume (GMV), the functional connectivity (FC) brain patterns of the region of interest, and behavioral data to identify suicide attempters. The results identified three latent classes of psychological pain in MDD patients: the low pain class (18.9%), the painful feeling class (37.2%), and the pain avoidance class (43.9%). Furthermore, the proportion of suicide attempters with high pain avoidance was the highest. The accuracy of multimodality classifiers (63%-92%) was significantly higher than that of brain-only classifiers (56%-85%) and behavior-only classifiers (64%-73%). Pain avoidance ranked first in the optimal feature set of the suicide attempt classification model. The crucial brain imaging features were FC between the left amygdala and right insula, right orbitofrontal and left thalamus, left anterior cingulate cortex and left insula, right orbitofrontal, amygdala, and the GMV of right thalamus. Additionally, the optimal feature set, including pain avoidance and crucial brain patterns of psychological pain neural circuits, was provided for the identification of suicide attempters.
No takes yet. Share an insight, caveat, or question.
Hao et al. (2022) conducted an observational in Major depressive disorder (MDD). Multimodality machine learning classifiers (pain avoidance, GMV, FC) vs. Brain-only and behavior-only classifiers was evaluated on Accuracy of identifying suicide attempters. Multimodality machine learning classifiers incorporating pain avoidance and brain imaging features identified suicide attempters with 63%-92% accuracy, outperforming single-modality classifiers.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: