Multivariate linear approach to fMRI data in stroop task performance in depression

Abstract The study aimed to assess the discriminative capacity of a machine learning algorithm in distinguishing between individuals with Major Depressive Disorder and healthy controls based on a dataset collected during the performance of a Stroop Color and Word Test combined with an n-back component in functional magnetic resonance imaging. A total of 50 participants were recruited, including 24 patients with depression and 26 healthy controls. The analysis employed a multivariate linear model, which identified two principal components characterized by their eigenvalues. The key finding of our study highlights the distinct contribution of eigenvalues, as represented in the principal components, to brain signatures with a strong capacity to differentiate between the two diagnostic groups examined for depression and healthy controls. Moreover, the results present a fresh network-level perspective, emphasizing the intricate interactions among different brain networks in major depression disorder. These findings support prior research indicating disruptions in sensory processing, cognitive control, and emotional regulation in Major Depressive Disorder. The results provide a novel, network-level perspective on these alterations, emphasizing the intricate interplay between sensory, cognitive, and emotional processes. Understanding these network dynamics may offer valuable insights into the neural mechanisms of Major Depressive Disorder and inform targeted interventions aimed at restoring functional connectivity and improving symptom management.