Integrating biobehavioral information to predict mood disorder suicide risk
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 suicida...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-10-01
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| Series: | Brain, Behavior, & Immunity - Health |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666354622000850 |
| _version_ | 1811281412744019968 |
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| author | Nicholas A. Jackson Mbemba M. Jabbi |
| author_facet | Nicholas A. Jackson Mbemba M. Jabbi |
| author_sort | Nicholas A. Jackson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T01:33:25Z |
| format | Article |
| id | doaj.art-8f4c0333fd784cf5bfb4a77a37bcfe57 |
| institution | Directory Open Access Journal |
| issn | 2666-3546 |
| language | English |
| last_indexed | 2024-04-13T01:33:25Z |
| publishDate | 2022-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Brain, Behavior, & Immunity - Health |
| spelling | doaj.art-8f4c0333fd784cf5bfb4a77a37bcfe572022-12-22T03:08:27ZengElsevierBrain, Behavior, & Immunity - Health2666-35462022-10-0124100495Integrating biobehavioral information to predict mood disorder suicide riskNicholas A. Jackson0Mbemba M. Jabbi1Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA; Institute for Neuroscience, The University of Texas at Austin, USADepartment of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA; Mulva Clinics for the Neurosciences; Institute for Neuroscience, The University of Texas at Austin, USA; Department of Psychology, The University of Texas at Austin, USA; Center for Learning and Memory, The University of Texas at Austin, USA; Corresponding author. Department of Psychiatry and Behavioral Sciences, Dell Medical School, The University of Texas at Austin, USA.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.http://www.sciencedirect.com/science/article/pii/S2666354622000850Mood disordersBrainBodyInflammationEnvironmental adversitySuicide |
| spellingShingle | Nicholas A. Jackson Mbemba M. Jabbi Integrating biobehavioral information to predict mood disorder suicide risk Brain, Behavior, & Immunity - Health Mood disorders Brain Body Inflammation Environmental adversity Suicide |
| title | Integrating biobehavioral information to predict mood disorder suicide risk |
| title_full | Integrating biobehavioral information to predict mood disorder suicide risk |
| title_fullStr | Integrating biobehavioral information to predict mood disorder suicide risk |
| title_full_unstemmed | Integrating biobehavioral information to predict mood disorder suicide risk |
| title_short | Integrating biobehavioral information to predict mood disorder suicide risk |
| title_sort | integrating biobehavioral information to predict mood disorder suicide risk |
| topic | Mood disorders Brain Body Inflammation Environmental adversity Suicide |
| url | http://www.sciencedirect.com/science/article/pii/S2666354622000850 |
| work_keys_str_mv | AT nicholasajackson integratingbiobehavioralinformationtopredictmooddisordersuiciderisk AT mbembamjabbi integratingbiobehavioralinformationtopredictmooddisordersuiciderisk |