Brain Structural Changes in the Course of Major Depressive Disorder: A Multilevel Modeling Approach to Longitudinal Imaging Data
Year: 2025
Type: preprint
Abstract: Major Depressive Disorder (MDD) affects more than 300 million people in the world and shows an increasing trend in prevalence (World Health Organization, 2017). Following the first episode, about 15-35% of patients with MDD develop recurrent episodes within the first years (Bukh et al., 2016). Less than half of all patients with MDD remain symptom-free for two years after recovery (Kanai et al., 2003). Moreover, the number of lifetime episodes, severity of preceding episode and presence of subclinical residual symptoms have been identified as risk factors to experience further recurrent episodes (Keller & Boland, 1998; Kennedy et al., 2003; Pettit et al., 2006). Taken together, these factors contribute to the accumulation of disease burden and the development of long-term chronicity of MDD (Hardeveld et al., 2013). Structural neuroimaging techniques may contribute to our understanding of the underlying neural mechanisms of reoccurrence in MDD (Kang & Cho, 2020). Subsequently, this could facilitate relapse prognosis and potentially advance maintenance treatment. Cross-sectional neuroimaging studies, including meta-analyses from international consortia (e.g., ENIGMA), suggest brain structural differences between MDD patients and healthy controls (HC; Gray et al., 2020; Schmaal et al., 2016, 2017), whereas effect sizes are small (Winter et al., 2022). Reductions in gray matter volume (GMV) and cortical thickness in brain areas such as the hippocampus (Campbell et al., 2004; Schmaal et al., 2016; McKinnon et al., 2009), insula (Lai & Wu, 2014; Stratmann et al., 2014; H. Zhang et al., 2016) and the prefrontal cortex (Bora et al., 2012; Schmaal et al., 2017; Zhang et al., 2018) are reported most frequently in association with MDD. These morphometric changes seem to be associated with the course of disease, specifically the number of recurrent episodes and duration of illness (Lemke, Romankiewicz, et al., 2022; McKinnon et al., 2009; Stratmann et al., 2014; Treadway et al., 2015). However, cross-sectional studies are restricted to correlative statements and fail to explain the direct interplay between recurrence of MDD and neural changes. Longitudinal studies in larger, well-characterized samples are needed to classify these changes into risk factors, correlates of the acute depressive state and consequences of prior depressive episodes. In previous longitudinal studies, brain structural alterations in regions, such as the dorsolateral prefrontal cortex (DLPFC), insula, hippocampus, and anterior cingulate cortex were observed (Dohm et al., 2017). Studies have reported a greater decline in GMV in these regions in non-remitters compared to patients whose MDD was in remission at follow-up assessment (Frodl et al., 2008; Phillips et al., 2015; Taylor et al., 2014). Vice versa, some studies reported an increase of GMV and cortical thickness in these regions with achieved remission (Hou et al., 2012; Phillips et al., 2015; Zaremba et al., 2018). Nonetheless, longitudinal research focusing exclusively on remission status at follow-up neglect the course of illness between scans, which is essential for accessing the link between brain alterations and relapse. A few studies investigated morphological changes as a function of relapse during follow-up interval (Frodl et al., 2008; Soriano-Mas et al., 2011), while some additionally controlled for confounding variables such as psychopharmacological treatment (Lemke, Klute, et al., 2022; Zaremba et al., 2018). These studies found that depressive relapse, as a distinct marker of disease progression during the interscan interval, is specifically linked to decline of GMV and cortical thickness and surface area in the insula and DLPFC. A loss of GMV in the insula and hippocampus has further been demonstrated in patients with severe courses of affective disorders, characterized by a hospitalization during a nine-year follow-up (Förster et al., 2023). Taken together, the findings provide first evidence of the negative impact of disease progression on the morphology of these brain regions. However, the studies share two key limitations. Firstly, grouping in dependence of experiencing at least one relapse does not account for variations in length of depressive episodes and thus disregards the duration the depressive state might affect the brain. Secondly, the majority of longitudinal imaging studies are restricted to two time points which frame a follow-up interval and used statistical models that evaluated the effect of relapse by comparing group means (Dohm et al., 2017; Lemke, Klute, et al., 2022; Zaremba et al., 2018). Given the broad heterogeneity of MDD disease course (Steinert et al., 2014), a statistical model that accounts for underlying individual trajectories over multiple time points may be more suitable. Rather than focusing on overall differences between related means of a given outcome variable across all participants, multilevel models estimate an underlying trajectory across all time points within each participant (Bollen & Curran, 2006). Moreover, multilevel models can provide a more nuanced approach to exploring potential cause-and-effect relationships by accounting for individual differences and time-related effects more effectively (Raudenbush, 2001). The lack of longitudinal imaging data in adult patients with MDD comprising multiple scans per individual over several years represents a crucial gap in the literature. This data in combination with differentiated assessment of the clinical course within follow-up intervals is indispensable to model and understand individual trajectories of brain structure in the long-term course of MDD. So far, little research analyzed gray matter trajectories associated with self-reported symptoms of depression over multiple scan waves in community samples of children and adolescents (Bos et al., 2018; Luby et al., 2016; Luking et al., 2022). One study indicated accelerated cortical thinning in the frontal lobe related to depressive symptoms (Bos et al., 2018) while others report a decline for global GMV and thickness (Luby et al., 2016). Nonetheless, generalization to adult MDD is questionable and the studies vary in their selection of ROIs and assessment of depressive symptoms. To fill this gap, we present the first longitudinal study that investigates individual trajectories of brain structure (GMV and cortical thickness) in the DLPFC, insula and hippocampus in association with duration in MDD including 3 to 7 scans per person covering up to 12 years. Our current investigation uses data from an ongoing multimodal longitudinal study of neurobiology in affective disorders. Participants with and without a diagnosis of MDD are re-assessed every two years undergoing MRI and clinical measurements. Patients with MDD were hospitalized at baseline assessment and recurrence of depressive episodes was determined by trained personnel at all assessments in a clinical interview. Due to common challenges of longitudinal studies, e.g. the correlation between repeated measures on the same person, irregularly timed data and most importantly missing data (Garcia & Marder, 2017), a multilevel modeling approach will be applied. To this end, we will first analyze associations of baseline GMV and cortical thickness of the DLPFC, insula and hippocampus with lifetime duration in MDD (analysis 1a and 1b). Then, duration in MDD per interval will be tested as a time varying predictor for GMV and cortical thickness of the three ROIs in multilevel models (MLM) including minimum three scans per person (analysis 2a and 2b). Finally, we rerun these models and account for psychopharmacological treatment effects by adding medication as an additional predictor (analysis 3a and 3b).
Source: OSF Preprints (OSF Preprints)
Authors: Joscha Böhnlein, Jochen Bauer, Dominik Grotegerd, Tim Hahn, Anna Kraus, Alea Bexten, Janik Goltermann, Susanne Meinert, Verena Enneking, Elisabeth Leehr, Elisabeth Schrammen, Katharina Dohm, Udo Dannlowski
Language: English
Subfield: Pharmacology (medical)
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7111786870