Electroconvulsive therapy (ECT) is associated with volumetric enlargements of corticolimbic brain regions. However, the pattern of whole-brain structural alterations following ECT remains unresolved. Here, we examined the longitudinal effects of ECT on global and local variations in gray matter, white matter, and ventricle volumes in patients with major depressive disorder as well as predictors of ECT-related clinical response. Longitudinal magnetic resonance imaging and clinical data from the Global ECT-MRI Research Collaboration (GEMRIC) were used to investigate changes in white matter, gray matter, and ventricle volumes before and after ECT in 328 patients experiencing a major depressive episode. In addition, 95 nondepressed control subjects were scanned twice. We performed a mega-analysis of single subject data from 14 independent GEMRIC sites. Volumetric increases occurred in 79 of 84 gray matter regions of interest. In total, the cortical volume increased by mean ± SD of 1.04 ± 1.03% (Cohen's d = 1.01, p < .001) and the subcortical gray matter volume increased by 1.47 ± 1.05% (d = 1.40, p < .001) in patients. The subcortical gray matter increase was negatively associated with total ventricle volume (Spearman's rank correlation ρ = -.44, p < .001), while total white matter volume remained unchanged (d = -0.05, p = .41). The changes were modulated by number of ECTs and mode of electrode placements. However, the gray matter volumetric enlargements were not associated with clinical outcome. The findings suggest that ECT induces gray matter volumetric increases that are broadly distributed. However, gross volumetric increases of specific anatomically defined regions may not serve as feasible biomarkers of clinical response.

Recent research suggests that neuroplastic and neuroinflammatory changes may account for the mode of action of electroconvulsive therapy (ECT), although extant data do not allow for a clear disambiguation between these two hypotheses. Multimodal neuroimaging approaches (for example, combining structural and metabolic information) may help in clarifying this issue. Here we aimed to assess longitudinal changes in (i) regional gray matter (GM) volumes and (ii) hippocampal metabolite concentrations throughout an acute course of bitemporal ECT, as well as (iii) to determine the association between imaging changes and clinical improvement. We assessed 12 patients with treatment-resistant depression (TRD) at four time points (pre-treatment, after the first ECT session, after the ninth ECT session and 15 days after ECT course completion) and 10 healthy participants at two time points, 5 weeks apart. Patients with TRD showed bilateral medial temporal lobe (MTL) and perigenual anterior cingulate cortex volume increases. Left MTL volume increase was associated with (i) a hippocampal N-acetylaspartate concentration decrease, (ii) a hippocampal Glutamate+Glutamine concentration increase and (iii) significant clinical improvement. The observed findings are, in part, compatible with both neuroplastic and neuroinflammatory changes induced by ECT. We postulate that such phenomena may be interrelated, therefore reconciling the neuroplasticity and neuroinflammatory hypotheses of ECT action.

Recent longitudinal neuroimaging studies in patients with electroconvulsive therapy (ECT) suggest local effects of electric stimulation (lateralized) occur in tandem with global seizure activity (generalized). We used electric field (EF) modeling in 151 ECT treated patients with depression to determine the regional relationships between EF, unbiased longitudinal volume change, and antidepressant response across 85 brain regions. The majority of regional volumes increased significantly, and volumetric changes correlated with regional electric field (t = 3.77, df = 83, r = 0.38, p=0.0003). After controlling for nuisance variables (age, treatment number, and study site), we identified two regions (left amygdala and left hippocampus) with a strong relationship between EF and volume change (FDR corrected p<0.01). However, neither structural volume changes nor electric field was associated with antidepressant response. In summary, we showed that high electrical fields are strongly associated with robust volume changes in a dose-dependent fashion.

Emotion-regulation strategies are understood to influence food intake. This study examined the neurophysiological underpinnings of negative emotion processing and emotion regulation in individuals with excess weight compared to normal-weight controls. Fifteen participants with excess-weight (body mass index >25) and sixteen normal-weight controls (body mass index 18–25) performed an emotion-regulation task during functional magnetic resonance imaging. Participants were exposed to 24 negative affective or neutral pictures that they were instructed to Observe (neutral pictures), Maintain (sustain the emotion elicited by negative pictures) or Regulate (down-regulate the emotion provoked by negative pictures through previously trained reappraisal techniques). When instructed to regulate negative emotions by means of cognitive reappraisal, participants with excess weight displayed persistently heightened activation in the right anterior insula. Decreased responsivity was also found in right anterior insula, the orbitofrontal cortex and cerebellum during negative emotion experience in participants with excess weight. Psycho-physiological interaction analyses showed that excess-weight participants had decreased negative functional coupling between the right anterior insula and the right dlPFC, and the bilateral dmPFC during cognitive reappraisal. Our findings support contentions that excess weight is linked to an abnormal pattern of neural activation and connectivity during the experience and regulation of negative emotions, with the insula playing a key role in these alterations. We posit that ineffective regulation of emotional states contributes to the acquisition and preservation of excess weight.

The role of the oral microbiome in mental health has recently been appreciated within the proposed oral-brain axis. This study examined the structure and composition of the salivary microbiome in a large-scale population-based cohort of individuals reporting mental health symptoms (n = 306) compared to mentally healthy controls (n = 164) using 16S rRNA sequencing. Mental health symptoms were evaluated using validated questionnaires and included depression, anxiety, and posttraumatic stress disorder (PTSD), with accompanying periodontal outcomes. Participants also indicated current or previous diagnoses of anxiety, depression, periodontitis, and gingivitis. Mental and periodontal health variables influenced the overall composition of the oral microbiome. PTSD symptoms correlated with a lower clr-transformed relative abundance of Haemophilus sputorum and a higher clr-transformed relative abundance of Prevotella histicola. The clr-transformed relative abundance of P. histicola was also positively associated with depressive scores and negatively associated with psychological quality of life. Anxiety disorder diagnosis was associated with a lower clr-transformed relative abundance of Neisseria elongate and a higher clr-transformed relative abundance of Oribacterium asaccharolyticum. A higher clr-transformed relative abundance of Shuttleworthia and lower clr-transformed relative abundance of Capnocytophaga were evident in those who reported a clinical periodontitis diagnosis. Higher Eggerthia and lower Haemophilus parainfluenzae clr-transformed relative abundances were associated with reported clinical periodontitis diagnoses and psychotherapeutic efficacy. Functional prediction analysis revealed a potential role for tryptophan metabolism/degradation in the oral-brain axis, which was confirmed by lower plasma serotonin levels across symptomatic groups. This study sheds light on the intricate interplay between oral microbiota, periodontal and mental health outcomes, and a potential role for tryptophan metabolism in the proposed oral-brain axis, emphasizing the need for further exploration to pave the way for novel therapeutic interventions and predicting therapeutic response.