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The hippocampus and amygdala have been implicated in the pathophysiology and treatment of major depressive disorder (MDD). Preclinical models suggest that stress-related changes in these regions can be reversed by antidepressants, including ketamine. Clinical studies have identified reduced volumes in MDD that are thought to be potentiated by early life stress and worsened by repeated depressive episodes. This study used 3T and 7T structural magnetic resonance imaging data to examine longitudinal changes in hippocampal and amygdalar subfield volumes associated with ketamine treatment. Data were drawn from a previous double-blind, placebo-controlled, crossover trial of healthy volunteers (HVs) unmedicated individuals with treatment-resistant depression (TRD) (3T: 18 HV, 26 TRD, 7T: 17 HV, 30 TRD) who were scanned at baseline and twice following either a 40 min IV ketamine (0.5 mg/kg) or saline infusion (acute: 1-2 days, interim: 9-10 days post infusion). No baseline differences were noted between the two groups. At 10 days post-infusion, a slight increase was observed between ketamine and placebo scans in whole left amygdalar volume in individuals with TRD. No other differences were found between individuals with TRD and HVs at either field strength. These findings shed light on the timing of ketamine's effects on cortical structures., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects., (© 2024. The Author(s).)

Major depressive disorder (MDD) is associated with altered global brain connectivity (GBC), as assessed via resting-state functional magnetic resonance imaging (rsfMRI). Previous studies found that antidepressant treatment with ketamine normalized aberrant GBC changes in the prefrontal and cingulate cortices, warranting further investigations of GBC as a putative imaging marker. These results were obtained via global signal regression (GSR). This study is an independent replication of that analysis using a separate dataset. GBC was analyzed in 28 individuals with MDD and 22 healthy controls (HCs) at baseline, post-placebo, and post-ketamine. To investigate the effects of preprocessing, three distinct pipelines were used: (1) regression of white matter (WM)/cerebrospinal fluid (CSF) signals only (BASE); (2) WM/CSF + GSR (GSR); and (3) WM/CSF + physiological parameter regression (PHYSIO). Reduced GBC was observed in individuals with MDD only at baseline in the anterior and medial cingulate cortices, as well as in the prefrontal cortex only after regressing the global signal. Ketamine had no effect compared to baseline or placebo in either group in any pipeline. PHYSIO did not resemble GBC preprocessed with GSR. These results concur with several studies that used GSR to study GBC. Further investigations are warranted into disease-specific components of global fMRI signals that may drive these results and of GBCr as a potential imaging marker in MDD.

The glutamatergic modulator ketamine has striking and rapid antidepressant effects in major depressive disorder (MDD), but its mechanism of action remains unknown. Proton magnetic resonance spectroscopy (1H-MRS) is the only non-invasive method able to directly measure glutamate levels in vivo; in particular, glutamate and glutamine metabolite concentrations are separable by 1H-MRS at 7T. This double-blind, placebo-controlled, crossover study that included 1 H-MRS scans at baseline and at 24 h post ketamine and post-placebo infusions sought to determine glutamate levels in the pregenual anterior cingulate (pgACC) of 20 medication-free MDD subjects and 17 healthy volunteers (HVs) 24 h post ketamine administration, and to evaluate any other measured metabolite changes, correlates, or predictors of antidepressant response. Metabolite levels were compared at three scan times (baseline, post-ketamine, and post-placebo) in HVs and MDD subjects at 7T using a 1 H-MRS sequence specifically optimized for glutamate. No significant between-group differences in 1 H-MRS-measured metabolites were observed at baseline. Antidepressant response was not predicted by baseline glutamate levels. Our results suggest that any infusion-induced increases in glutamate at the 24-h post ketamine time point were below the sensitivity of the current technique; that these increases may occur in different brain regions than the pgACC; or that subgroups of MDD subjects may exist that have a differential glutamate response to ketamine.