Every year, there are more than 40,000 suicides and ten times as many suicide attempts in the United States. Suicide is the second leading cause of death in young people ages 15 to 24 years old.

Unfortunately, the death and injury rates due to suicide attempts have not declined over the last two decades, suggesting that we need a paradigm shift in prevention to meaningfully reduce the suicide rate. Better and more successful prevention requires improving our knowledge regarding the causes of suicidal behavior.

Conte Center Focus

The Conte Center at Columbia University and The New York State Psychiatric Institute is funded by the National Institute of Mental Health. The Conte Center has defined six highly integrated and complementary Projects. Each project brings different methodologies to the study of suicide risk: postmortem, animal paradigms, PET and MRI neuroimaging, clinical, and statistical modeling approaches. To make this research possible, four Cores provide administrative, clinical, laboratory, and data management infrastructure. Through a more detailed understanding of the neurobiology of suicide, our overall goal is to discover ways to reduce or prevent suicide risk.

Genes and childhood adversity combine to create a predisposition to suicidal behavior in a subgroup of psychiatric patients. If we can identify who is at heightened risk we can better target prevention efforts. We employ a multidisciplinary approach to study how reported childhood adversity interacts with genes to mold the predisposition to suicidal behavior.

Project 1

Neurobiology of Suicide: Childhood Adversity, Neuroinflammation and Genomics

Led by Dr. Mark Underwood, this project uses brain tissue donated by families to study factors that may contribute to suicide. Information obtained from family members, in a method called psychological autopsy, helps us to examine the relationship of childhood adversity to gene expression or function in neurons.  These postmortem studies examine the relationships of childhood adversity to proinflammatory markers and neuroplasticity in suicides compared with depressed individuals with no history of suicide attempts and nonpsychiatric controls.

Project 2

Animal Models of Suicide: Behavior, Neurobiological and Molecular Phenotypes

This preclinical project, directed by Dr. Rene Hen, uses early life stress mouse paradigms to examine effects of childhood adversity vs enriched social environments on DNA methylation and gene expression in key brain regions related to cognition and emotional regulation. We study effects of maternal deprivation on anxious and aggressive behaviors, and on the expression or function of genes, neuronal growth and death, and brain circuits. This project also tests the potential benefit of an enriched parenting and caring environment as a potential way of reversing the adverse effects of prolonged maternal deprivation.

Project 3

PET Neuroimaging in Vivo in Mood Disorders and Suicidal Behavior

Under the direction of Dr. John Mann, the positron emission tomography (PET) project will perform dynamic neuroimaging of the serotonin system and inflammation to study brain function in depressed patients, including suicide attempters and nonattempters, as well as healthy volunteers.

Project 4

Cognitive Phenotype Neural Circuitry in Vivo In Mood Disorders and Suicidal Behavior

Dr. Kevin Ochsner’s team will use functional MRI to quantify regional brain responses related to cognitive control of emotion, believed to be of key importance in understanding suicidal behavior.

Project 5

Stress, Inflammation, Aggression and Emotion Regulation in Suicidal Behavior

Headed by Dr. Barbara Stanley, Project 5 will perform a clinical characterization of all the participants in the two brain imaging projects, examining how emotional and physiological responses to stress relate to cognition and suicidal behavior.

Project 6

Statistical Models of Suicidal Behavior and Brain Biology Using Large Data Sets

Led by Dr. R. Todd Ogden, the statistical project will extend existing methods using high-dimensional imaging, cytokine profiles and genomic data to build models that give meaningful insight into relationships between predictors and suicide risk.