Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.

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.

An Innovative Random-Forest-Based Model to Assess the Health Impacts of Regular Commuting Using Non-Invasive Wearable Sensors.

Regular commutes to work can cause chronic stress, which in turn can cause a physical and emotional reaction. The recognition of mental stress in its earliest stages is very necessary for effective clinical treatment. This study investigated the impact of commuting on human health based on qualitative and quantitative measures. The quantitative measures included electroencephalography (EEG) and blood pressure (BP), as well as weather temperature, while qualitative measures were established from the PANAS questionnaire, and included age, height, medication, alcohol status, weight, and smoking status. This study recruited 45 (n) healthy adults, including 18 female and 27 male participants. The modes of commute were bus (n = 8), driving (n = 6), cycling (n = 7), train (n = 9), tube (n = 13), and both bus and train (n = 2). The participants wore non-invasive wearable biosensor technology to measure EEG and blood pressure during their morning commute for 5 days in a row. A correlation analysis was applied to find the significant features associated with stress, as measured by a reduction in positive ratings in the PANAS. This study created a prediction model using random forest, support vector machine, naive Bayes, and K-nearest neighbor. The research results show that blood pressure and EEG beta waves were significantly increased, and the positive PANAS rating decreased from 34.73 to 28.60. The experiments revealed that measured systolic blood pressure was higher post commute than before the commute. For EEG waves, the model shows that the EEG beta low power exceeded alpha low power after the commute. Having a fusion of several modified decision trees within the random forest helped increase the performance of the developed model remarkably. Significant promising results were achieved using random forest with an accuracy of 91%, while K-nearest neighbor, support vector machine, and naive Bayes performed with an accuracy of 80%, 80%, and 73%, respectively.

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Adjunctive home-based transcranial direct current stimulation treatment for major depression with real-time remote supervision: An open-label, single-arm feasibility study with long term outcomes.

Current treatments for major depressive disorder (MDD) have limited effectiveness and acceptability. Transcranial direct current stimulation (tDCS) is a novel non-invasive brain stimulation method that has demonstrated treatment efficacy in MDD. tDCS requires daily sessions, however clinical trials have been conducted in research centers requiring repeated visits. As tDCS is portable and safe, it could be provided at home. We developed a home-based protocol with real-time supervision, and we examined the clinical outcomes, acceptability and feasibility. Participants were 26 MDD (19 women), mean age 40.9 ± 14.2 years, in current depressive episode of moderate to severe severity (mean 17-item Hamilton Rating Scale for Depression (HAMD) score 19.12 ± 2.12). tDCS was provided in a bilateral frontal montage, F3 anode, F4 cathode, 2 mA, each session 30 min, in a 6-week trial, for a total 21 sessions. Participants maintained their current treatment (antidepressant medication, psychotherapy, or were enrolled in online CBT). Two tDCS device brands were used, and a research team member was present in person or by real-time video call at each session. 92.3% MDD participants (n = 24) completed the 6-week treatment. Attrition rate was 7.7%. There was a significant improvement in depressive symptoms following treatment (mean HAMD 5.33 ± 2.33), which was maintained at 6 months (mean HAMD 5.43 ± 2.73). Acceptability was endorsed as "very acceptable" or "quite acceptable" by all participants. Due to the open-label feasibility design, efficacy findings are preliminary. In summary, home-based tDCS with real-time supervision was associated with significant clinical improvements and high acceptability which were maintained in the long term.

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression.

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×108) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant-based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen f2 = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen f2 = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer's Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions.

Brain Correlates of Suicide Attempt in 18,925 Participants Across 18 International Cohorts.

BACKGROUND: Neuroimaging studies of suicidal behavior have so far been conducted in small samples, prone to biases and false-positive associations, yielding inconsistent results. The ENIGMA-MDD Working Group aims to address the issues of poor replicability and comparability by coordinating harmonized analyses across neuroimaging studies of major depressive disorder and related phenotypes, including suicidal behavior. METHODS: Here, we pooled data from 18 international cohorts with neuroimaging and clinical measurements in 18,925 participants (12,477 healthy control subjects and 6448 people with depression, of whom 694 had attempted suicide). We compared regional cortical thickness and surface area and measures of subcortical, lateral ventricular, and intracranial volumes between suicide attempters, clinical control subjects (nonattempters with depression), and healthy control subjects. RESULTS: We identified 25 regions of interest with statistically significant (false discovery rate < .05) differences between groups. Post hoc examinations identified neuroimaging markers associated with suicide attempt including smaller volumes of the left and right thalamus and the right pallidum and lower surface area of the left inferior parietal lobe. CONCLUSIONS: This study addresses the lack of replicability and consistency in several previously published neuroimaging studies of suicide attempt and further demonstrates the need for well-powered samples and collaborative efforts. Our results highlight the potential involvement of the thalamus, a structure viewed historically as a passive gateway in the brain, and the pallidum, a region linked to reward response and positive affect. Future functional and connectivity studies of suicidal behaviors may focus on understanding how these regions relate to the neurobiological mechanisms of suicide attempt risk.

Is tDCS a potential first line treatment for major depression?

Transcranial direct current stimulation (tDCS) is a novel treatment option for major depression which could be provided as a first-line treatment. tDCS is a non-invasive form of transcranial stimulation which changes cortical tissue excitability by applying a weak (0.5-2 mA) direct current via scalp electrodes. Anodal and cathodal stimulation leads to depolarisation and hyperpolarisation, respectively, and cumulative effects are observed with repeated sessions. The montage in depression most often involves anodal stimulation to the left dorsolateral prefrontal cortex. Rates of clinical response, remission, and improvements in depressive symptoms following a course of active tDCS are greater in comparison to a course of placebo sham-controlled tDCS. In particular, the largest treatment effects are evident in first episode and recurrent major depression, while minimal effects have been observed in treatment-resistant depression. The proposed mechanism is neuroplasticity at the cellular and molecular level. Alterations in neural responses have been found at the stimulation site as well as subcortically in prefrontal-amygdala connectivity. A possible mediating effect could be cognitive control in emotion dysregulation. Additional beneficial effects on cognitive impairments have been reported, which would address an important unmet need. The tDCS device is portable and can be used at home. Clinical trials are required to establish the efficacy, feasibility and acceptability of home-based tDCS treatment and mechanisms.

Dehydration in older people: A systematic review of the effects of dehydration on health outcomes, healthcare costs and cognitive performance.

OBJECTIVE: To systematically examine the effect of dehydration on health outcomes, identify associated financial costs and consider impacts on cognitive performance in older adults. DESIGN: A systematic review of English-language articles via OVID using MEDLINE, PsychINFO, EMBASE, and others, to March 2018. Included studies examined the relationship between hydration status and health, care costs or cognitive outcome. SETTING: Cross sectional and cohort data from studies reporting on dehydration in older adults. PARTICIPANTS: Adults aged 60 years and older. MEASUREMENTS: Independent quality ratings were assessed for all extracted articles. RESULTS: Of 1684 articles screened, 18 papers (N = 33,707) met inclusion criteria. Participants were recruited from hospital settings, medical long-term care centres and the community dwelling population. Data were synthesised using a narrative summary. Mortality rates were higher in dehydrated patients. Furthermore, health outcomes, including frailty, bradyarrhythmia, transient ischemic attacks, oral health and surgery recovery are linked to and worsened by dehydration. Length of hospital stay, either as a principal or secondary diagnosis, is greater in those with dehydration, compared to those who are euhydrated. Finally, neurocognitive functioning may be impacted by dehydration. There are issues with study design, inconsistency in hydration status measurement and different measures used for outcome assessment. CONCLUSION: Dehydration in older people is associated with increased mortality, poorer course of illness and increased costs for health services. In addition, there is some, but sparse evidence that dehydration in older people is linked to poorer cognitive performance. Intervention studies should test strategies for reducing dehydration in older adults.

Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group.

Major depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in adult MDD patients, and whether this process is associated with clinical characteristics in a large multicenter international dataset. We performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 19 samples worldwide. Healthy brain aging was estimated by predicting chronological age (18-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 952 male and 1236 female controls from the ENIGMA MDD working group. The learned model coefficients were applied to 927 male controls and 986 depressed males, and 1199 female controls and 1689 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted "brain age" and chronological age was calculated to indicate brain-predicted age difference (brain-PAD). On average, MDD patients showed a higher brain-PAD of +1.08 (SE 0.22) years (Cohen's d = 0.14, 95% CI: 0.08-0.20) compared with controls. However, this difference did not seem to be driven by specific clinical characteristics (recurrent status, remission status, antidepressant medication use, age of onset, or symptom severity). This highly powered collaborative effort showed subtle patterns of age-related structural brain abnormalities in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the clinical value of these brain-PAD estimates.

Motor adaptation and internal model formation in a robot-mediated forcefield.

Background: Motor adaptation relies on error-based learning for accurate movements in changing environments. However, the neurophysiological mechanisms driving individual differences in performance are unclear. Transcranial magnetic stimulation (TMS)-evoked potential can provide a direct measure of cortical excitability. Objective: To investigate cortical excitability as a predictor of motor learning and motor adaptation in a robot-mediated forcefield. Methods: A group of 15 right-handed healthy participants (mean age 23 years) performed a robot-mediated forcefield perturbation task. There were two conditions: unperturbed non-adaptation and perturbed adaptation. TMS was applied in the resting state at baseline and following motor adaptation over the contralateral primary motor cortex (left M1). Electroencephalographic (EEG) activity was continuously recorded, and cortical excitability was measured by TMS-evoked potential (TEP). Motor learning was quantified by the motor learning index. Results: Larger error-related negativity (ERN) in fronto-central regions was associated with improved motor performance as measured by a reduction in trajectory errors. Baseline TEP N100 peak amplitude predicted motor learning (P = 0.005), which was significantly attenuated relative to baseline (P = 0.0018) following motor adaptation. Conclusions: ERN reflected the formation of a predictive internal model adapted to the forcefield perturbation. Attenuation in TEP N100 amplitude reflected an increase in cortical excitability with motor adaptation reflecting neuroplastic changes in the sensorimotor cortex. TEP N100 is a potential biomarker for predicting the outcome in robot-mediated therapy and a mechanism to investigate psychomotor abnormalities in depression.

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-derived neurotrophic factor association with amygdala response in major depressive disorder.

BACKGROUND: Brain-derived neurotrophic factor (BDNF) has an essential role in synaptic plasticity and neurogenesis. BDNF mediates amygdala-dependent learning for both aversive and appetitive emotional memories. The expression of BDNF in limbic regions is posited to contribute the development of depression, and amygdala responsivity is a potential marker of depressive state. METHODS: The present study examined the relationship between platelet BDNF levels and amygdala volume and function in major depressive disorder (MDD). Participants were 23 MDD (mean age 38.9 years) and 23 healthy controls (mean age 38.8 years). All participants were recruited from the community. MDD participants were in a current depressive episode of moderate severity and medication-free. Amygdala responses were acquired during a functional MRI task of implicit emotional processing with sad facial expressions. RESULTS: Significant correlation was observed between platelet BDNF levels and left amygdala responses, but no significant correlations were found with right amygdala responses or with amygdala volumes. LIMITATIONS: Interactions with neuroprotective as well as neurotoxic metabolites in the kyneurenine pathway were not examined. CONCLUSIONS: Relationship between BDNF levels and amygdala responsivity to emotionally salient stimuli in MDD could reflect the importance of BDNF in amygdala-dependent learning with clinical implications for potential pathways for treatment.

Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort.

BACKGROUND: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. METHODS: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. RESULTS: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. CONCLUSIONS: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.

Widespread Morphometric Abnormalities in Major Depression: Neuroplasticity and Potential for Biomarker Development.

Major depression is common and debilitating. Identifying neurobiological subtypes that comprise the disorder and predict clinical outcome are key challenges. Genetic and environmental factors leading to major depression are expressed in neural structure and function. Volumetric decreases in gray matter have been demonstrated in corticolimbic circuits involved in emotion regulation. MR imaging observable abnormalities reflect cytoarchitectonic alterations within a local neuroendocrine milieu with systemic effects. Multivariate pattern analysis offers the potential to identify the neurobiological subtypes and predictors of clinical outcome. It is essential to characterize disease heterogeneity by incorporating data-driven inductive and symptom-based deductive approaches in an iterative process.

Addressing heterogeneity (and homogeneity) in treatment mechanisms in depression and the potential to develop diagnostic and predictive biomarkers.

It has been 10 years since machine learning was first applied to neuroimaging data in psychiatric disorders to identify diagnostic and prognostic markers at the level of the individual. Proof of concept findings in major depression have since been extended in international samples and are beginning to include hundreds of samples from multisite data. Neuroimaging provides the unique capability to detect an acute depressive state in major depression, while we would not expect perfect classification with current diagnostic criteria which are based solely on clinical features. We review developments and the potential impact of heterogeneity, as well as homogeneity, on classification for diagnosis and prediction of clinical outcome. It is likely that there are distinct biotypes which comprise the disorder and which predict clinical outcome. Neuroimaging-based biotypes could aid in identifying the illness in individuals who are unable to recognise their illness and perhaps to identify the treatment resistant form early in the course of the illness. We propose that heterogeneous symptom profiles can arise from a limited number of neural biotypes and that apparently heterogeneous clinical outcomes include a common baseline predictor and common mechanism of treatment. Baseline predictors of clinical outcome reflect factors which indicate the general likelihood of response as well as those which are selective for a particular form of treatment. Irrespective of the mechanism, the capacity for response will moderate the outcome, which includes inherent models of interpersonal relationships that could be associated with genetic risk load and represented by patterns of functional and structural neural correlates as a predictive biomarker. We propose that methods which directly address heterogeneity are essential and that a synergistic combination could bring together data-driven inductive and symptom-based deductive approaches. Through this iterative process, major depression can develop from being syndrome characterized by a collection of symptoms to a disease with an identifiable pathophysiology.

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.

Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults: systematic review and network meta-analysis.

OBJECTIVE: To estimate the comparative clinical efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults. DESIGN: Systematic review with pairwise and network meta-analysis. DATA SOURCES: Electronic search of Embase, PubMed/Medline, and PsycINFO up to 8 May 2018, supplemented by manual searches of bibliographies of several reviews (published between 2009 and 2018) and included trials. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Clinical trials with random allocation to electroconvulsive therapy (ECT), transcranial magnetic stimulation (repetitive (rTMS), accelerated, priming, deep, and synchronised), theta burst stimulation, magnetic seizure therapy, transcranial direct current stimulation (tDCS), or sham therapy. MAIN OUTCOME MEASURES: Primary outcomes were response (efficacy) and all cause discontinuation (discontinuation of treatment for any reason) (acceptability), presented as odds ratios with 95% confidence intervals. Remission and continuous depression severity scores after treatment were also examined. RESULTS: 113 trials (262 treatment arms) that randomised 6750 patients (mean age 47.9 years; 59% women) with major depressive disorder or bipolar depression met the inclusion criteria. The most studied treatment comparisons were high frequency left rTMS and tDCS versus sham therapy, whereas recent treatments remain understudied. The quality of the evidence was typically of low or unclear risk of bias (94 out of 113 trials, 83%) and the precision of summary estimates for treatment effect varied considerably. In network meta-analysis, 10 out of 18 treatment strategies were associated with higher response compared with sham therapy: bitemporal ECT (summary odds ratio 8.91, 95% confidence interval 2.57 to 30.91), high dose right unilateral ECT (7.27, 1.90 to 27.78), priming transcranial magnetic stimulation (6.02, 2.21 to 16.38), magnetic seizure therapy (5.55, 1.06 to 28.99), bilateral rTMS (4.92, 2.93 to 8.25), bilateral theta burst stimulation (4.44, 1.47 to 13.41), low frequency right rTMS (3.65, 2.13 to 6.24), intermittent theta burst stimulation (3.20, 1.45 to 7.08), high frequency left rTMS (3.17, 2.29 to 4.37), and tDCS (2.65, 1.55 to 4.55). Network meta-analytic estimates of active interventions contrasted with another active treatment indicated that bitemporal ECT and high dose right unilateral ECT were associated with increased response. All treatment strategies were at least as acceptable as sham therapy. CONCLUSIONS: These findings provide evidence for the consideration of non-surgical brain stimulation techniques as alternative or add-on treatments for adults with major depressive episodes. These findings also highlight important research priorities in the specialty of brain stimulation, such as the need for further well designed randomised controlled trials comparing novel treatments, and sham controlled trials investigating magnetic seizure therapy.

Anodal transcranial direct current stimulation over the right dorsolateral prefrontal cortex enhances reflective judgment and decision-making.

BACKGROUND: Accounts of cognitive processes in judgment and decision-making are frequently based on a dual-process framework, which reflects two qualitatively different types of processing: intuitive (Type 1) and analytical (Type 2) processes. OBJECTIVE: The present study investigated the effects of bilateral transcranial direct current stimulation (tDCS) to the dorsolateral prefrontal cortex (DLPFC) on judgment and decision-making performance. METHODS: Participants received anodal tDCS stimulation to the right DLPFC, left DLPFC or sham. There were 3 tasks: vignettes measuring heuristic thinking, belief bias syllogisms, and the cognitive reflection test (CRT), a measure of the ability to inhibit automatic responses to reach a correct solution. Fifty-four participants (mean age = 24.63 ±â€¯4.46 years; 29 females) were recruited. RESULTS: Results showed that anodal tDCS to the right DLPFC was associated with an increase in cognitive reflection performance (Type 2 processing) as compared to left DLPFC and to sham. Logic thinking was reduced following anodal tDCS to the left DLPFC. CONCLUSION: These findings are broadly consistent with a dual process framework, and cannot be explained by differences in cognitive ability and thinking style. The results demonstrate the involvement of the right DLPFC in cognitive reflection, and suggest the possibility of improving cognitive performance through tDCS.

Associations between polygenic risk scores for four psychiatric illnesses and brain structure using multivariate pattern recognition.

Psychiatric illnesses are complex and polygenic. They are associated with widespread alterations in the brain, which are partly influenced by genetic factors. There have been some attempts to relate polygenic risk scores (PRS) - a measure of the overall genetic risk an individual carries for a disorder - to brain structure using univariate methods. However, PRS are likely associated with distributed and covarying effects across the brain. We therefore used multivariate machine learning in this proof-of-principle study to investigate associations between brain structure and PRS for four psychiatric disorders; attention deficit-hyperactivity disorder (ADHD), autism, bipolar disorder and schizophrenia. The sample included 213 individuals comprising patients with depression (69), bipolar disorder (33), and healthy controls (111). The five psychiatric PRSs were calculated based on summary data from the Psychiatric Genomics Consortium. T1-weighted magnetic resonance images were obtained and voxel-based morphometry was implemented in SPM12. Multivariate relevance vector regression was implemented in the Pattern Recognition for Neuroimaging Toolbox (PRoNTo). Across the whole sample, a multivariate pattern of grey matter significantly predicted the PRS for autism (r = 0.20, pFDR = 0.03; MSE = 4.20 × 10-5, pFDR = 0.02). For the schizophrenia PRS, the MSE was significant (MSE = 1.30 × 10-5, pFDR = 0.02) although the correlation was not (r = 0.15, pFDR = 0.06). These results lend support to the hypothesis that polygenic liability for autism and schizophrenia is associated with widespread changes in grey matter concentrations. These associations were seen in individuals not affected by these disorders, indicating that this is not driven by the expression of the disease, but by the genetic risk captured by the PRSs.