SSRI/SNRI and long COVID in children and adolescents with neuropsychiatric conditions: a cohort study from the RECOVER Initiative.

Over 11,000 papers have been published to date to investigate the structural and functional neuroimaging correlates of depression (based on a PubMed search for the keyword string ‘neuroimaging depression’). However, recent meta-analyses attempting to collate results across previous studies have reported null findings, indicating a troubling lack of consistency in neuroimaging correlates of depression across the substantive existing literature. Sampling variability arising from underpowered study samples that comprise each meta-analysis may explain these null findings, and such within-cohort sample size challenges are often present even in large-scale studies of the neural correlates of depression. As such, a conclusive mapping of the neuroimaging correlates of depression remains unknown. In this study, we aimed to comprehensively identify the most reliable structural and resting state functional neuroimaging correlates of depression across six large-scale datasets. Beyond sampling variability in underpowered studies, there are several other challenges that may contribute to the observed inconsistencies in the literature on neuroimaging correlates of depression. For example, there are multiple self-reported phenotypic definitions of depression in common use, which are broadly based on either symptom severity or trait-level personality-based susceptibility indexed using the neuroticism personality trait. Although high neuroticism is a strong predictor of depression, it is also linked to other psychiatric disorders, including anxiety and substance use, and is therefore less specific to depression symptomatology than symptom severity measures. Furthermore, the literature on neuroimaging correlates of depression has largely focused on higher-order cortical systems and subcortical regions, which may have systematically resulted in under-reporting in other regions of the brain. To address these challenges, we determined the unbiased whole-brain spatial distribution of the structural and functional neuroimaging correlates reliably associated with symptom severity versus personality-based susceptibility phenotypes of depression. We used six state-of-the-art large-scale datasets to comprehensively map the parcellated structural and resting state functional neuroimaging correlates of depression. Each dataset was analyzed separately and estimates were subsequently meta-analytically combined to establish the most reliable neuroimaging correlates of depression robust to sampling variability, phenotypic definitions and regional bias (Fig. ). We purposefully chose to treat the six datasets included in this study separately rather than harmonizing all data into one mega sample. This approach was selected because perfect harmonization is not feasible, especially when factors of importance (such as age range) are fully co-linear with dataset separation. Furthermore, non-removal of study differences (for example, in relation to preprocessing, sample definition, depression instruments and so on) offers a testbed for the type of realistic study-to-study variation observed in the literature. Notably, sufficiently large datasets with gold standard clinician-based diagnostics are lacking, so this study leveraged self-reported measures of symptom severity and personality-based susceptibility as phenotypes of depression.