D. Hoehn

Development of the Brain's Default Mode Network from Wakefulness to Slow Wave Sleep

Falling asleep is paralleled by a loss of conscious awareness and reduced capacity to process external stimuli. Little is known on sleep-associated changes of spontaneously synchronized anatomical networks as detected by resting-state functional magnetic resonance imaging (rs-fMRI). We employed functional connectivity analysis of rs-fMRI series obtained from 25 healthy participants, covering all non-rapid eye movement (NREM) sleep stages. We focused on the default mode network (DMN) and its anticorrelated network (ACN) that are involved in internal and external awareness during wakefulness. Using independent component analysis, cross-correlation analysis (CCA), and intraindividual dynamic network tracking, we found significant changes in DMN/ACN integrity throughout the NREM sleep. With increasing sleep depth, contributions of the posterior cingulate cortex (PCC)/retrosplenial cortex (RspC), parahippocampal gyrus, and medial prefrontal cortex to the DMN decreased. CCA revealed a breakdown of corticocortical functional connectivity, particularly between the posterior and anterior midline node of the DMN and the DMN and the ACN. Dynamic tracking of the DMN from wakefulness into slow wave sleep in a single subject added insights into intraindividual network fluctuations. Results resonate with a role of the PCC/RspC for the regulation of consciousness. We further submit that preserved corticocortical synchronization could represent a prerequisite for maintaining internal and external awareness.

Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group.

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen’s d=−0.14, % difference=−1.24). This effect was driven by patients with recurrent MDD (Cohen’s d=−0.17, % difference=−1.44), and we detected no differences between first episode patients and controls. Age of onset ⩽21 was associated with a smaller hippocampus (Cohen’s d=−0.20, % difference=−1.85) and a trend toward smaller amygdala (Cohen’s d=−0.11, % difference=−1.23) and larger lateral ventricles (Cohen’s d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status.

Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group

The neuro-anatomical substrates of major depressive disorder (MDD) are still not well understood, despite many neuroimaging studies over the past few decades. Here we present the largest ever worldwide study by the ENIGMA (Enhancing Neuro Imaging Genetics through Meta-Analysis) Major Depressive Disorder Working Group on cortical structural alterations in MDD. Structural T1-weighted brain magnetic resonance imaging (MRI) scans from 2148 MDD patients and 7957 healthy controls were analysed with harmonized protocols at 20 sites around the world. To detect consistent effects of MDD and its modulators on cortical thickness and surface area estimates derived from MRI, statistical effects from sites were meta-analysed separately for adults and adolescents. Adults with MDD had thinner cortical gray matter than controls in the orbitofrontal cortex (OFC), anterior and posterior cingulate, insula and temporal lobes (Cohen’s d effect sizes: −0.10 to −0.14). These effects were most pronounced in first episode and adult-onset patients (>21 years). Compared to matched controls, adolescents with MDD had lower total surface area (but no differences in cortical thickness) and regional reductions in frontal regions (medial OFC and superior frontal gyrus) and primary and higher-order visual, somatosensory and motor areas (d: −0.26 to −0.57). The strongest effects were found in recurrent adolescent patients. This highly powered global effort to identify consistent brain abnormalities showed widespread cortical alterations in MDD patients as compared to controls and suggests that MDD may impact brain structure in a highly dynamic way, with different patterns of alterations at different stages of life.

Structural brain alterations in major depression: findings from the ENIGMA Major Depressive Disorder Working Group

Background: Patterns of structural brain alterations in major depressive disorder (MDD) remain unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. Therefore, we initiated the ENIGMA-MDD Working Group to identify robust imaging markers of MDD using coordinated standardized image processing and statistical analysis protocols. Here, we investigated subcortical volume alterations in MDD in the largest sample to date using an individual participant data (IPD) based metaanalysis approach.