Association of disease course and brain structural alterations in major depressive disorder

Abstract

Introduction The investigation of disease course‐associated brain structural alterations in Major Depressive Disorder (MDD) have resulted in heterogeneous findings, possibly due to low reliability of single clinical variables used for defining disease course. The present study employed a principal component analysis (PCA) on multiple clinical variables to investigate effects of cumulative lifetime illness burden on brain structure in a large and heterogeneous sample of MDD patients. Methods Gray matter volumes (GMV) was estimated in n = 681 MDD patients (mean age: 35.87 years; SD = 12.89; 66.6% female) using voxel‐based‐morphometry. Five clinical variables were included in a PCA to obtain components reflecting disease course to associate resulting components with GMVs. Results The PCA yielded two main components: Hospitalization reflected by patients' frequency and duration of inpatient treatment and Duration of Illness reflected by the frequency and duration of depressive episodes. Hospitalization revealed negative associations with bilateral dorsolateral prefrontal cortex (DLPFC) and left insula volumes. Duration of Illness showed significant negative associations with left hippocampus and right DLPFC volumes. Results in the DLPFC and hippocampus remained significant after additional control for depressive symptom severity, psychopharmacotherapy, psychiatric comorbidities, and remission status. Conclusion This study shows that a more severe and chronic lifetime disease course in MDD is associated with reduced volume in brain regions relevant for executive and cognitive functions and emotion regulation in a large sample of patients representing the broad heterogeneity of MDD disease course. These findings were only partly influenced by other clinical characteristics (e.g., remission status, psychopharmacological treatment).

Brain structural correlates of recurrence following the first episode in patients with major depressive disorder

“…Notably, only a small sample of 27 FED patients and 17 HC was investigated. The results of this study stand in contrast to former literature reporting GMV alterations in FED patients compared with HC [21][22][23][24], and GMV changes associated with disease progression [8][9][10][11][12][13][14][15][16].…”

“…While cross-sectional research in MDD repeatedly showed that decreased gray matter volumes (GMV) and (sub-)cortical thickness are associated with more severe lifetime disease trajectories [6][7][8][9][10], the direct interplay between neural changes and recurrence remains unclear. Previous longitudinal studies reported morphometric changes [11], for example, in the dorsolateral prefrontal cortex (DLPFC), insula, hippocampus, and anterior cingulate cortex, showing greater GMV decline in dependence of more detrimental disease courses [12][13][14][15][16].…”

Major depression, physical health and molecular senescence markers abnormalities

“…We leveraged a relatively large LLD sample (n = 426) with SASP protein measurements. Following previous studies demonstrating the utility of integrating various variables [32][33][34] , we used factor analyses to group clinical variables. We then tested our hypothesis that the resulting clinical factors would be associated with the SASP index.…”

A Systematic Evaluation of Machine Learning-based Biomarkers for Major Depressive Disorder across Modalities

“…associating neuroimaging markers with long-term disease trajectories. [8,[56][57][58] Likewise, investigating symptoms rather than syndromes has been promoted lately, with network theory of psychopathology providing one conceptual framework possibly able to model symptom dynamics independent of psychiatric category. [59] Indeed, our results regarding correlations of misclassification frequency provide support for associations between symptom severity and neurobiological markers, suggesting that patients with higher levels of current symptoms, lower global functioning and more unfavourable disease courses in the past are easier to detect and correctly classify.…”