Doctoral Thesis: Exploring Cortical Thickness Variations in Depression
Introduction: Depression is a complex mental health condition with far-reaching consequences, affecting millions globally. While extensive research has explored its psychological manifestations, understanding the neurobiological underpinnings, particularly cortical thickness variations, remains paramount. This doctoral thesis endeavors to unravel the intricate interplay between cortical thickness alterations and depressive symptomatology, aiming to illuminate the neural substrates of depression.
Problem Statement: Conventional studies predominantly juxtapose cortical thickness differences between individuals diagnosed with major depression and those deemed healthy controls. However, a critical gap persists in comprehending cortical thickness dynamics across individuals with varying degrees of depressive symptoms within the general population. This thesis seeks to address this gap by investigating cortical thickness changes in relation to depressive symptom severity beyond the binary classification of diagnosed depression versus non-depression.
Methodology Overview:
Cortical Mapping and Region Segmentation: The brain’s cortical surface is meticulously partitioned into 34 regions per hemisphere, resulting in 68 distinct regions for analysis. These regions serve as the fundamental units of investigation to evaluate cortical thickness changes using sophisticated neuroimaging techniques.
Initial Analysis with Binary Depressive Symptomatology Variable: The analysis commences with the utilization of a binary variable, “Cut16MediDiagn,” encapsulating the presence or absence of depressive symptomatology based on specific criteria:
- CES-D score ≥ 16
- Use of antidepressant medication
- Clinical diagnosis of depression
Description: The initial phase involves conducting regression analyses, where the dependent variable is cortical thickness in a specific region (“Cortical Thickness (Z.n. rh_isthmuscingulate)”), and the independent variable is “Cut16MediDiagn”. The models progress from a crude model to more nuanced models integrating additional covariates like age, sex, education years, polypharmacy, marital status, comorbidities, and employment status.
Linear Assessment of Depressive Symptomatology: To explore the linear relationship between cortical thickness and depressive symptom severity, CES-D scores ranging from 0 to 34 are utilized as the independent variable “depmw” across similar regression models as in the binary analysis.
Description: Linear regression models are employed, where the cortical thickness serves as the dependent variable, and the CES-D scores act as the independent variable, with adjustments made for covariates like age, sex, education years, polypharmacy, marital status, comorbidities, and employment status.
Exploring CES-D Score Stability: The analysis extends to examine how cortical thickness varies concerning the stability of CES-D scores. Different patterns of CES-D score stability are investigated, including continuously high scores, continuously low scores, fluctuating scores, and stable scores around the cutoff of 17. This exploration necessitates revisiting the original variable “kat_depri” to assess its impact on cortical thickness variations.
Description: Regression models are constructed, where the dependent variable remains cortical thickness, while the independent variable is “kat_depri”. Covariates such as age and sex are included to account for potential confounding factors.
Through these meticulous analyses, this thesis aims to provide valuable insights into the neural correlates of depressive symptomatology, paving the way for more targeted interventions and personalized treatments for depression.
Conclusion: By employing sophisticated statistical techniques and leveraging neuroimaging data, this thesis endeavors to provide valuable insights into the nuanced relationship between cortical thickness variations and depressive symptomatology across diverse populations. The findings have the potential to inform future research directions and contribute to the development of more effective diagnostic and therapeutic interventions for depression.