Integrating biobehavioral information to predict mood disorder suicide risk.
Brain Behav Immun Health
; 24: 100495, 2022 Oct.
Article
em En
| MEDLINE
| ID: mdl-35990401
ABSTRACT
The will to live and the ability to maintain one's well-being are crucial for survival. Yet, almost a million people die by suicide globally each year (Aleman and Denys, 2014), making premature deaths due to suicide a significant public health problem (Saxena et al., 2013). The expression of suicidal behaviors is a complex phenotype with documented biological, psychological, clinical, and sociocultural risk factors (Turecki et al., 2019). From a brain disease perspective, suicide is associated with neuroanatomical, neurophysiological, and neurochemical dysregulations of brain networks involved in integrating and contextualizing cognitive and emotional regulatory behaviors. From a symptom perspective, diagnostic measures of dysregulated mood states like major depressive symptoms are associated with over sixty percent of suicide deaths worldwide (Saxena et al., 2013). This paper reviews the neurobiological and clinical phenotypic correlates for mood dysregulations and suicidal phenotypes. We further propose machine learning approaches to integrate neurobiological measures with dysregulated mood symptoms to elucidate the role of inflammatory processes as neurobiological risk factors for suicide.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Brain Behav Immun Health
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos