Neuropsychological and functional outcomes in recent-onset major depression, bipolar disorder and schizophrenia-spectrum disorders: a longitudinal cohort study

Functional disability is the lead contributor to burden of mental illness. Cognitive deficits frequently limit functional recovery, although whether changes in cognition and disability are longitudinally associated in recent-onset individuals remains unclear. Using a prospective, cohort design, 311 patients were recruited and assessed at baseline. One hundred and sixty-seven patients met eligibility criteria (M = 21.5 years old, s.d. = 4.8) and returned for follow-up (M = 20.6 months later, s.d. = 7.8). Two-hundred and thirty participants were included in the final analysis, comprising clinically stable patients with major depression (n = 71), bipolar disorder (BD; n = 61), schizophrenia-spectrum disorders (n = 35) and 63 healthy controls. Neuropsychological functioning and self-rated functional disability were examined using mixed-design, repeated-measures analysis, across diagnoses and cognitive clusters, covarying for relevant confounds. Clinical, neuropsychological and functional changes did not differ between diagnoses (all P40.05). Three reliable neuropsychological subgroups emerged through cluster analysis, characterized by psychomotor slowing, improved sustained attention, and improved verbal memory. Controlling for diagnosis and changes in residual symptoms, clusters with improved neuropsychological functioning observed greater reductions in functional disability than the psychomotor slowing cluster, which instead demonstrated a worsening in disability (Po0.01). Improved sustained attention was independently associated with greater likelihood of follow-up employment (Po0.01). Diagnosis of BD uniquely predicted both follow-up employment and independent living. Neuropsychological course appears to be independently predictive of subjective and objective functional outcomes. Importantly, cognitive phenotypes may reflect distinct pathophysiologies shared across major psychiatric conditions, and be ideal targets for personalized early intervention

Frontal lobe changes occur early in the course of affective disorders in young people

<p>Abstract</p> <p>Background</p> <p>More severe and persistent forms of affective disorders are accompanied by grey matter loss in key frontal and temporal structures. It is unclear whether such changes precede the onset of illness, occur early in the course or develop gradually with persistence or recurrence of illness. A total of 47 young people presenting with admixtures of depressive and psychotic symptoms were recruited from specialist early intervention services along with 33 age matched healthy control subjects. All participants underwent magnetic resonance imaging and patients were rated clinically as to current stage of illness. Twenty-three patients were identified as being at an early 'attenuated syndrome' stage, while the remaining were rated as having already reached the 'discrete disorder' or 'persistent or recurrent illness' stage. Contrasts were carried out between controls subjects and patients cohorts with attenuated syndromes and discrete disorders, separately.</p> <p>Results</p> <p>The patients that were identified as having a discrete or persisting disorder demonstrated decreased grey matter volumes within distributed frontal brain regions when contrasted to both the control subjects as well as those patients in the attenuated syndrome stage. Overall, patients who were diagnosed as more advanced in terms of the clinical stage of their illness, exhibited the greatest grey matter volume loss of all groups.</p> <p>Conclusions</p> <p>This study suggests that, in terms of frontal grey matter changes, a major transition point may occur in the course of affective illness between early attenuated syndromes and later discrete illness stages.</p

Hippocampal function in schizophrenia and bipolar disorder

Background. The hippocampus plays a central role in memory formation. There is considerable evidence of abnormalities in hippocampal structure and function in schizophrenia, which may differentiate it from bipolar disorder. However, no previous studies have compared hippocampal activation in schizophrenia and bipolar disorder directly. Method. Fifteen patients with schizophrenia, 14 patients with bipolar disorder and 14 healthy comparison subjects took part in the study. Subjects performed a face name pair memory task during functional magnetic resonance imaging (fMRI). Differences in blood oxygen level-dependent (BOLD) activity were determined during encoding and retrieval of the face name pairs. Results. The patient groups showed significant differences in hippocampal and prefrontal cortex (PFC) activation during face name pair learning. During encoding, patients with schizophrenia showed decreased anterior hippocampal activation relative to subjects with bipolar disorder, whereas patients with bipolar disorder showed decreased dorsal PFC activation relative to patients with schizophrenia. During retrieval, patients with schizophrenia showed greater activation of the dorsal PFC than patients with bipolar disorder. Patients with schizophrenia also differed from healthy control subjects in the activation of several brain regions, showing impaired superior temporal cortex activation during encoding and greater dorsal PFC activation during retrieval. These effects were evident despite matched task performance. Conclusions. Patients with schizophrenia showed deficits in hippocampal activation during a memory task relative to patients with bipolar disorder. The disorders were further distinguished by differences in PFC activation. The results demonstrate that these disorders can distinguished at a group level using non-invasive neuroimaging

Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder : results from the ENIGMA MDD Working Group

It has been difficult to find robust brain structural correlates of the overall severity of major depressive disorder (MDD). We hypothesized that specific symptoms may better reveal correlates and investigated this for the severity of insomnia, both a key symptom and a modifiable major risk factor of MDD. Cortical thickness, surface area and subcortical volumes were assessed from T1-weighted brain magnetic resonance imaging (MRI) scans of 1053 MDD patients (age range 13-79 years) from 15 cohorts within the ENIGMA MDD Working Group. Insomnia severity was measured by summing the insomnia items of the Hamilton Depression Rating Scale (HDRS). Symptom specificity was evaluated with correlates of overall depression severity. Disease specificity was evaluated in two independent samples comprising 2108 healthy controls, and in 260 clinical controls with bipolar disorder. Results showed that MDD patients with more severe insomnia had a smaller cortical surface area, mostly driven by the right insula, left inferior frontal gyrus pars triangularis, left frontal pole, right superior parietal cortex, right medial orbitofrontal cortex, and right supramarginal gyrus. Associations were specific for insomnia severity, and were not found for overall depression severity. Associations were also specific to MDD; healthy controls and clinical controls showed differential insomnia severity association profiles. The findings indicate that MDD patients with more severe insomnia show smaller surfaces in several frontoparietal cortical areas. While explained variance remains small, symptom-specific associations could bring us closer to clues on underlying biological phenomena of MDD

No Alterations of Brain Structural Asymmetry in Major Depressive Disorder: An ENIGMA Consortium Analysis

OBJECTIVE: Asymmetry is a subtle but pervasive aspect of the human brain, and it may be altered in several psychiatric conditions. MRI studies have shown subtle differences of brain anatomy between people with major depressive disorder and healthy control subjects, but few studies have specifically examined brain anatomical asymmetry in relation to this disorder, and results from those studies have remained inconclusive. At the functional level, some electroencephalography studies have indicated left fronto-cortical hypoactivity and right parietal hypoactivity in depressive disorders, so aspects of lateralized anatomy may also be affected. The authors used pooled individual-level data from data sets collected around the world to investigate differences in laterality in measures of cortical thickness, cortical surface area, and subcortical volume between individuals with major depression and healthy control subjects. METHODS: The authors investigated differences in the laterality of thickness and surface area measures of 34 cerebral cortical regions in 2,256 individuals with major depression and 3,504 control subjects from 31 separate data sets, and they investigated volume asymmetries of eight subcortical structures in 2,540 individuals with major depression and 4,230 control subjects from 32 data sets. T1-weighted MRI data were processed with a single protocol using FreeSurfer and the Desikan-Killiany atlas. The large sample size provided 80% power to detect effects of the order of Cohen's d=0.1. RESULTS: The largest effect size (Cohen's d) of major depression diagnosis was 0.085 for the thickness asymmetry of the superior temporal cortex, which was not significant after adjustment for multiple testing. Asymmetry measures were not significantly associated with medication use, acute compared with remitted status, first episode compared with recurrent status, or age at onset. CONCLUSIONS: Altered brain macro-anatomical asymmetry may be of little relevance to major depression etiology in most cases

ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

A key objective in the field of translational psychiatry over the past few decades has been to identify the brain correlates of major depressive disorder (MDD). Identifying measurable indicators of brain processes associated with MDD could facilitate the detection of individuals at risk, and the development of novel treatments, the monitoring of treatment effects, and predicting who might benefit most from treatments that target specific brain mechanisms. However, despite intensive neuroimaging research towards this effort, underpowered studies and a lack of reproducible findings have hindered progress. Here, we discuss the work of the ENIGMA Major Depressive Disorder (MDD) Consortium, which was established to address issues of poor replication, unreliable results, and overestimation of effect sizes in previous studies. The ENIGMA MDD Consortium currently includes data from 45 MDD study cohorts from 14 countries across six continents. The primary aim of ENIGMA MDD is to identify structural and functional brain alterations associated with MDD that can be reliably detected and replicated across cohorts worldwide. A secondary goal is to investigate how demographic, genetic, clinical, psychological, and environmental factors affect these associations. In this review, we summarize findings of the ENIGMA MDD disease working group to date and discuss future directions. We also highlight the challenges and benefits of large-scale data sharing for mental health research

Brain-age prediction:Systematic evaluation of site effects, and sample age range and size

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5–90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8–80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9–25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5–40 and 40–90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.</p