This study investigated the potential of incorporating neuroanatomical biomarkers into suicidality research in those with Dissociative Identity Disorder (DID), more specifically variability in cortical thickness, to better provide a foundation in DID research that could lead to improved development of treatments, preventative measures, and better assessments for suicide and self-harm risk in those with DID. Ninety-seven participants were recruited with 24 controls and 73 patients diagnosed with Dissociative Identity Disorder and comorbid Post-Traumatic Stress Disorder (PTSD) via a psychiatric hospital in the northeastern United States. We used a standard set of qualitative assessments to measure characteristics such as PTSD, frequency of childhood maltreatment, dissociative disorder prevalence, dissociative symptom severity, depression severity, and suicidal tendencies in our patients. Patients were placed into a magnetic resonance imaging machine (MRI) for a structural image of their brains for no longer than ten minutes. We used machine learning to analyze the structural MRI images for abnormal cortical thickness in each patient and mine for patterns that correspond with the abnormal cortical thickness and their clinical assessment responses. We found nine regions with abnormal cortical thickness, all known to be associated with memory, visual processing, emotion regulation, language processing, social perception and cognition, and self-awareness. These regions have associations with both suicidality and DID symptomatologies. Providing this neuroanatomical basis allows us to provide a foundation to suicidality and DID research that was previously unavailable, in the hopes that future research will build upon our results to better treatments and preventative measures for those with DID.

• This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

Creator

• Linnell, Lilly-Beth

Publisher

• Worcester Polytechnic Institute

Identifier

• E-project-042722-214024
• 64766

Keyword

• Machine Learning
• Cortical Thickness
• Neuroanatomical Biomarkers
• Dissociative Identity Disorder
• Suicidality

Advisor

• Skorinko, Jeanine LM
• Nephew, Benjamin

Year

• 2022

Date created

• 2022-04-27

Resource type

• Major Qualifying Project

Major

• Psychological Science

Rights statement

• In Copyright