Increased Cortical Thickness in Patients With Major Depressive Disorder Following Antidepressant Treatment

Abstract

Background: Considering the slow-acting properties of traditional antidepressants, an important challenge in the field is the identification of early treatment response biomarkers. Reduced cortical thickness has been reported in neuroimaging studies of depression. However, little is known whether antidepressants reverse this abnormality. In this brief report, we investigated early cortical thickness changes following treatment with sertraline compared to placebo. Methods: Participants (n ¼ 215) with major depressive disorder were randomized to a selective serotonin reuptake inhibitor, sertraline, or to placebo. Structural magnetic resonance imaging scans were acquired at baseline and one week following treatment. Response was defined as at least 50% improvement in Hamilton rating scale for depression score at week 8. In a vertex-wise approach, we examined the effects of treatment, response, and treatment  response. Results: Following correction for multiple comparisons, we found a significant effect of treatment, with widespread increase in cortical thickness following sertraline compared to placebo. Clusters with increased thickness were found in the left medial prefrontal cortex, right medial and lateral prefrontal cortex, and within the right parieto-temporal lobes. There were no sertraline-induced cortical thinning, and no significant response effects or treatment  response interactions. Conclusion: Our findings suggest that cortical thickness abnormalities may be responsive to antidepressant treatment. However, a relationship between these early cortical changes and later treatment response was not demonstrated. Future studies would be needed to investigate whether those early effects are maintained at eight weeks and are associated with enhanced response.

Pretreatment Brain Connectome Fingerprint Predicts Treatment Response in Major Depressive Disorder

“… 33 , 34 MR scans were surface-based preprocessed by using a pipeline adapted from the HCP ( https://github.com/Washington-University/HCPpipelines ), 35 as reported elsewhere. 14 , 25 , 28 , 36 , 37 …”

“…33,34 MR scans were surface-based preprocessed by using a pipeline adapted from the HCP (https://gith ub.com/Washington-University/HCPpipelines), 35 as reported elsewhere. 14,25,28,36,37 In the preprocessing pipeline, FreeSurfer parcellation was used for structural scans. Slice timing correction, motion correction, intensity normalization, brain masking, and registration of fMRI images to structural MRI and standard template were performed.…”

“…18,26 There are two major advantages of the NRS approach: 1) it enables calculations of internal connectivity strength in all identifiable nodes within any FNs as well as external connectivity strength between all FNs; and 2) using the Akiki-Abdallah (AA) hierarchical atlas for whole-brain parcellation, "nodes" (modules or communities) are defined using subject-level clustering of functional networks and a reclustering network, allowing the large number of edges to be reduced, and thus reducing the number of statistical comparisons and leading to better interpretations of findings. 27,28 Leveraging this promising new approach, the current study employed the NRS predictive modelling (PM) methods to identify a functional connectivity signature of treatment response in patients with MDD randomized to sertraline or placebo. In the present study, Aim 1 was to identify a pretreatment CFP that predicts antidepressant response, regardless of treatment modality and Aim 2 was to identify a CFP that predicts better response to sertraline, compared to placebo.…”

“… 18 , 26 There are two major advantages of the NRS approach: 1) it enables calculations of internal connectivity strength in all identifiable nodes within any FNs as well as external connectivity strength between all FNs; and 2) using the Akiki-Abdallah (AA) hierarchical atlas for whole-brain parcellation, “nodes” (modules or communities) are defined using subject-level clustering of functional networks and a reclustering network, allowing the large number of edges to be reduced, and thus reducing the number of statistical comparisons and leading to better interpretations of findings. 27 , 28 …”

A robust and reproducible connectome fingerprint of ketamine is highly associated with the connectomic signature of antidepressants

“…Profiling the connectivity of all the brain's networks, a process termed functional connectome fingerprinting, has been successfully applied to predict behavior (e.g., [6]) and recently identified a unique connectome fingerprint (CFP) that predates and predicts response to antidepressants [7]. In the current study, we employed this predictive model approach to identify robust and reproducible functional connectivity signatures of ketamine.…”