Classification Of Major Depressive Disorder Research Articles

Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex

Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical assessment, are subjective and rely on healthcare professionals' expertise. Recently, there's growing interest in using Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to objectively understand MDD's neurobiology, complementing traditional diagnostics. The posterior cingulate cortex (PCC) is a pivotal brain region implicated in MDD which could be used to identify MDD from healthy controls. Thus, this study presents an intelligent approach based on rs-fMRI data to enhance the classification of MDD.Original rs-fMRI data were collected from a cohort of 430 participants, comprising 197 patients and 233 healthy controls. Subsequently, the data underwent preprocessing using DPARSF, and the amplitudes of low-frequency fluctuation values were computed to reduce data dimensionality and feature count. Then data associated with the PCC were extracted. After eliminating redundant features, various types of Support Vector Machines (SVMs) were employed as classifiers for intelligent categorization. Ultimately, we compared the performance of each algorithm, along with its respective optimal classifier, based on classification accuracy, true positive rate, and the area under the receiver operating characteristic curve (AUC-ROC). Upon analyzing the comparison results, we determined that the Random Forest (RF) algorithm, in conjunction with a sophisticated Gaussian SVM classifier, demonstrated the highest performance. Remarkably, this combination achieved a classification accuracy of 81.9 % and a true positive rate of 92.9 %.In conclusion, our study improves the classification of MDD by supplementing traditional methods with rs-fMRI and machine learning techniques, offering deeper neurobiological insights and aiding accuracy, while emphasizing its role as an adjunct to clinical assessment.

Characterizing major depressive disorder and substance use disorder using heatmaps and variable interactions: The utility of operant behavior and brain structure relationships.

Rates of depression and addiction have risen drastically over the past decade, but the lack of integrative techniques remains a barrier to accurate diagnoses of these mental illnesses. Changes in reward/aversion behavior and corresponding brain structures have been identified in those with major depressive disorder (MDD) and cocaine-dependence polysubstance abuse disorder (CD). Assessment of statistical interactions between computational behavior and brain structure may quantitatively segregate MDD and CD. Here, 111 participants [40 controls (CTRL), 25 MDD, 46 CD] underwent structural brain MRI and completed an operant keypress task to produce computational judgment metrics. Three analyses were performed: (1) linear regression to evaluate groupwise (CTRL v. MDD v. CD) differences in structure-behavior associations, (2) qualitative and quantitative heatmap assessment of structure-behavior association patterns, and (3) the k-nearest neighbor machine learning approach using brain structure and keypress variable inputs to discriminate groups. This study yielded three primary findings. First, CTRL, MDD, and CD participants had distinct structure-behavior linear relationships, with only 7.8% of associations overlapping between any two groups. Second, the three groups had statistically distinct slopes and qualitatively distinct association patterns. Third, a machine learning approach could discriminate between CTRL and CD, but not MDD participants. These findings demonstrate that variable interactions between computational behavior and brain structure, and the patterns of these interactions, segregate MDD and CD. This work raises the hypothesis that analysis of interactions between operant tasks and structural neuroimaging might aide in the objective classification of MDD, CD and other mental health conditions.

Classification accuracy of structural and functional connectomes across different depressive phenotypes

Abstract Phenotyping of major depressive disorder (MDD) can vary from study to study, which, together with heterogeneity of the disorder, may contribute to the inconsistent associations with neuroimaging features and underlie previous problems with machine-learning methods for MDD diagnostic applications. In this study, we examined the classification accuracy of structural and functional connectomes across different depressive phenotypes, including separating MDD subgroups into those with and without self-reported exposure to childhood trauma (CT) (one of the largest risk factors for MDD associated with brain development). We applied logistic ridge regression to classify control and MDD participants defined by six different MDD definitions in a large community-based sample (N=14,507). We used brain connectomic data based on six structural and two functional network weightings and conducted a comprehensive analysis to (i) explore how well different connectome modalities predict different MDD phenotypes commonly used in research, (ii) whether stratification of MDD based on self-reported exposure to childhood trauma (measured with the childhood trauma questionnaire (CTQ)) may improve the accuracies, and (iii) identify important predictive features across different MDD phenotypes. We found that functional connectomes outperformed structural connectomes as features for MDD classification across phenotypes. The highest accuracy of 64.8% (chance level 50.0%) was achieved in the Currently Depressed (defined by the presence of more than five symptoms of depression in the past 2 weeks) sample with additional CTQ criterion using partial correlation functional connectomes. The predictive feature overlap, measured using Jaccard index, indicated that there were neurobiological differences between MDD patients with and without childhood adversity. Further analysis of predictive features for different MDD phenotypes with hypergeometric tests revealed sensorimotor and visual subnetworks as important predictors of MDD. Our results suggest that differences in sensorimotor and visual subnetworks may serve as potential biomarkers of MDD.

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.

Privacy Preserving Collaboratively Training Framework for Classification of Major Depressive Disorder using Non-IID Three Channel Electroencephalogram

Major Depressive Disorder (MDD) is characterized by low mood, loss of interest and even suicidal ideation. Electroencephalogram based diagnosis of a variety of neurological conditions has been conducted in recent years using modern neurocomputing and deep learning approaches. Privacy is a high concern for medical data over distributed training scenarios. Hence, this paper aims to develop an EEG-based privacy-preserved system to identify MDD through the Federated Learning (FL) approach. In this study, training for 3-channel EEG-based MDD screening is introduced over FL using deep learning (DL) algorithms such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and One Dimensional Convolutional Neural Network (1D-CNN). A privacy-preserving solution for MDD patients utilizing FL is advantageous because clinical data is sensitive and most people do not want to share their personal information. Hence, the analysis of the comparative results for multiple applicable strategies including Independent and Identically Disturbed (IID) data, non-IID data, and algorithms provide proof that FL can be used to train DL models. The MODMA dataset of 3-channel EEG with 26 MDD and 29 non-MDD individuals is used in this paper. Future study on MDD has several potential areas of use and these FL approaches can also be coupled with new technologies.

Effects of the Hyperparameters on CNNs for MDD Classification Using Resting-State EEG

To monitor patients with depression, objective diagnostic tools that apply biosignals and exhibit high repeatability and efficiency should be developed. Although different models can help automatically learn discriminative features, inappropriate adoption of input forms and network structures may cause performance degradation. Accordingly, the aim of this study was to systematically evaluate the effects of convolutional neural network (CNN) architectures when using two common electroencephalography (EEG) inputs on the classification of major depressive disorder (MDD). EEG data for 21 patients with MDD and 21 healthy controls were obtained from an open-source database. Five hyperparameters (i.e., number of convolutional layers, filter size, pooling type, hidden size, and batch size) were then evaluated. Finally, Grad-CAM and saliency map were applied to visualize the trained models. When raw EEG signals were employed, optimal performance and efficiency were achieved as more convolutional layers and max pooling were used. Furthermore, when mixed features were employed, a larger hidden layer and smaller batch size were optimal. Compared with other complex networks, this configuration involves a relatively small number of layers and less training time but a relatively high accuracy. Thus, high accuracy (>99%) can be achieved in MDD classification by using an appropriate combination in a simple model.

Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram.

In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.

Estimation of Discriminative Multimodal Brain Network Connectivity Using Message-Passing-Based Nonlinear Network Fusion.

Effective estimation of brain network connectivity enables better unraveling of the extraordinary complexity interactions of brain regions and helps in auxiliary diagnosis of psychiatric disorders. Considering different modalities can provide comprehensive characterizations of brain connectivity, we propose the message-passing-based nonlinear network fusion (MP-NNF) algorithm to estimate multimodal brain network connectivity. In the proposed method, the initial functional and structural networks were computed from fMRI and DTI separately. Then, we update every unimodal network iteratively, making it more similar to the others in every iteration, and finally converge to one unified network. The estimated brain connectivities integrate complementary information from multiple modalities while preserving their original structure, by adding the strong connectivities present in unimodal brain networks and eliminating the weak connectivities. The effectiveness of the method was evaluated by applying the learned brain connectivity for the classification of major depressive disorder (MDD). Specifically, 82.18% classification accuracy was achieved even with the simple feature selection and classification pipeline, which significantly outperforms the competing methods. Exploration of brain connectivity contributed to MDD identification suggests that the proposed method not only improves the classification performance but also was sensitive to critical disease-related neuroimaging biomarkers.

Ensemble graph neural network model for classification of major depressive disorder using whole-brain functional connectivity.

Major depressive disorder (MDD) is characterized by impairments in mood and cognitive functioning, and it is a prominent source of global disability and stress. A functional magnetic resonance imaging (fMRI) can aid clinicians in their assessments of individuals for the identification of MDD. Herein, we employ a deep learning approach to the issue of MDD classification. Resting-state fMRI data from 821 individuals with MDD and 765 healthy controls (HCs) is employed for investigation. An ensemble model based on graph neural network (GNN) has been created with the goal of identifying patients with MDD among HCs as well as differentiation between first-episode and recurrent MDDs. The graph convolutional network (GCN), graph attention network (GAT), and GraphSAGE models serve as a base models for the ensemble model that was developed with individual whole-brain functional networks. The ensemble's performance is evaluated using upsampling and downsampling, along with 10-fold cross-validation. The ensemble model achieved an upsampling accuracy of 71.18% and a downsampling accuracy of 70.24% for MDD and HC classification. While comparing first-episode patients with recurrent patients, the upsampling accuracy is 77.78% and the downsampling accuracy is 71.96%. According to the findings of this study, the proposed GNN-based ensemble model achieves a higher level of accuracy and suggests that our model produces can assist healthcare professionals in identifying MDD.

Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network.

Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression. To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs). Prospective. A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium. A 3.0 T/resting-state functional MRI using the gradient echo sequence. A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (MSFS ) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (MTVF ), spatial variability features (MSVF ), and integrated temporal and spatial variability features with hybrid feature selection method (MHFS ). A linear regression model based on MSFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores. Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant. The model with MSFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r=0.640) and anxiety (r=0.616) in MDD. Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD. 2. Stage 2.

Classification of Major Depressive Disorder using Machine Learning on brain structure and functional connectivity

Timely identification of the risk of having Major Depressive Disorder (MDD) using the advanced machine learning (ML) approach has been popularized to generate neural indicators of MDD. However, past ML studies have usually employed a comprehensive whole-brain approach, resulting in high computational cost that is hardly affordable in practical and clinical contexts. In this study, we took an alternative approach by first conducting a meta-analysis to identify brain regions of interest (ROIs) where structural and/or functional alterations had been consistently reported in MDD patients. We then utilized a nonlinear multilayer deep neural network method to evaluate the classification accuracies of MDD patients (N = 41, 29 females) and healthy individuals (N = 20, 12 females) based on the structural volume and resting-state functional connectivity (rsFC) of the ROIs. We found that the ML model based on brain volumes, with the inferior frontal cortex (IFC), amygdala, and the pons receiving the highest weights, achieved higher classification accuracy and sensitivity relative to the model based on rsFC. For ethical reasons, people with MDD remained taking antidepressants at the time of the study. Our findings highlight the critical role of the pons-cortico-limbic network in MDD, which is a significant insight for early identification and diagnosis of the illness for timely intervention.

Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder.

BackgroundAlterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification.MethodsSeventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance.ResultsExperimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification.ConclusionThese findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings.

Classification of major depressive disorder using an attention-guided unified deep convolutional neural network and individual structural covariance network.

Major depressive disorder (MDD) is the second leading cause of disability worldwide. Currently, the structural magnetic resonance imaging-based MDD diagnosis models mainly utilize local grayscale information or morphological characteristics in a single site with small samples. Emerging evidence has demonstrated that different brain structures in different circuits have distinct developmental timing, but mature coordinately within the same functional circuit. Thus, establishing an attention-guided unified classification framework with deep learning and individual structural covariance networks in a large multisite dataset could facilitate developing an accurate diagnosis strategy. Our results showed that attention-guided classification could improve the classification accuracy from primary 75.1% to ultimate 76.54%. Furthermore, the discriminative features of regional covariance connectivities and local structural characteristics were found to be mainly located in prefrontal cortex, insula, superior temporal cortex, and cingulate cortex, which have been widely reported to be closely associated with depression. Our study demonstrated that our attention-guided unified deep learning framework may be an effective tool for MDD diagnosis. The identified covariance connectivities and structural features may serve as biomarkers for MDD.

An End-to-End Depression Recognition Method Based on EEGNet.

Major depressive disorder (MDD) is a common and highly debilitating condition that threatens the health of millions of people. However, current diagnosis of depression relies on questionnaires that are highly correlated with physician experience and hence not completely objective. Electroencephalography (EEG) signals combined with deep learning techniques may be an objective approach to effective diagnosis of MDD. This study proposes an end-to-end deep learning framework for MDD diagnosis based on EEG signals. We used EEG signals from 29 healthy subjects and 24 patients with severe depression to calculate Accuracy, Precision, Recall, F1-Score, and Kappa coefficient, which were 90.98%, 91.27%, 90.59%, and 81.68%, respectively. In addition, we found that these values were highest when happy-neutral face pairs were used as stimuli for detecting depression. Compared with exiting methods for EEG-based MDD classification, ours can maintain stable model performance without re-calibration. The present results suggest that the method is highly accurate for diagnosis of MDD and can be used to develop an automatic plug-and-play EEG-based system for diagnosing depression.

Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder.

Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.

Accurate classification of depression through optimized machine learning models on high-dimensional noisy data

MotivationDepressive disorders are highly prevalent and impairing psychiatric conditions with neurocognitive abnormalities, including reduced event-related potential (ERP) measures of reward processing and emotional reactivity. Accurate classification of Major Depressive Disorder (MDD) based on ERP data could help improve our understanding of these alterations and propel novel diagnostic or screening measures. However, it has been particularly challenging due to the lack of generalization for noisy raw data with small sample sizes. We aim to improve classification performance for MDD using noisy ERP datasets using machine learning (ML) techniques. ResultsWe have developed two optimizations in our ML-based analysis of ERP datasets: effective feature extraction in the preprocessing of high-dimensional noisy data and enhanced classification through ensemble ML models. Together with a carefully designed validation strategy, our techniques provide a highly accurate method for MDD classification even for ERP data that are limited in sample size, inherently noisy and high-dimensional in nature. Our experimental results demonstrate that our ML optimizations achieve great accuracy and nearly perfect sensitivity simultaneously, particularly in classifying data samples unseen during the training process, compared to prior studies that perform regression-based classifications. Supplementary informationA supplementary document on ERP data collection is available.

Translational application of neuroimaging in major depressive disorder: a review of psychoradiological studies.

Major depressive disorder (MDD) causes great decrements in health and quality of life with increments in healthcare costs, but the causes and pathogenesis of depression remain largely unknown, which greatly prevent its early detection and effective treatment. With the advancement of neuroimaging approaches, numerous functional and structural alterations in the brain have been detected in MDD and more recently attempts have been made to apply these findings to clinical practice. In this review, we provide an updated summary of the progress in translational application of psychoradiological findings in MDD with a specified focus on potential clinical usage. The foreseeable clinical applications for different MRI modalities were introduced according to their role in disorder classification, subtyping, and prediction. While evidence of cerebral structural and functional changes associated with MDD classification and subtyping was heterogeneous and/or sparse, the ACC and hippocampus have been consistently suggested to be important biomarkers in predicting treatment selection and treatment response. These findings underlined the potential utility of brain biomarkers for clinical practice.

Screening for depression in children and adolescents: a protocol for a systematic review update

BackgroundMajor depressive disorder is common, debilitating, and affects feelings, thoughts, mood, and behaviors. Childhood and adolescence are critical periods for the development of depression and adolescence is marked by an increased incidence of mental health disorders. This protocol outlines the planned scope and methods for a systematic review update that will evaluate the benefits and harms of screening for depression in children and adolescents.MethodsThis review will update a previously published systematic review by Roseman and colleagues. Eligible studies are randomized controlled trials (RCTs) assessing formal screening in primary care to identify children or adolescents not already self-reporting symptoms of, diagnosed with, or treated for depression. If no or only a single RCT is available, we will consider controlled studies without random assignment. Studies of participants with characteristics associated with an elevated risk of depression will be analyzed separately. Outcomes of interest are symptoms of depression, classification of major depressive disorder based on a validated diagnostic interview, suicidality, health-related quality of life, social function, impact on lifestyle behavior (e.g., substance use, school performance, lost time at work, or school), false-positive results, overdiagnosis, overtreatment, labeling, and other harms such as those arising from treatment. We will search MEDLINE, Embase, PsycINFO, CINAHL, the Cochrane Library, and grey literature sources. Two reviewers will independently screen the titles and abstracts using the liberal accelerated method. Full-text screening will be performed independently by two reviewers using pre-specified eligibility criteria. Data extraction and risk of bias assessments will be performed independently by two reviewers. Pre-planned analyses, including subgroup and sensitivity analyses, are detailed within this protocol. Two independent reviewers will assess and finalize through consensus the certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, and prepare GRADE evidence profiles and summary of findings tables for each outcome of interest.DiscussionThe systematic review will provide a current state of the evidence of benefits and harms of depression screening in children and adolescents. These findings will be used by the Canadian Task Force on Preventive Health Care to inform the development of recommendations on depression screening.Systematic review registrationPROSPERO CRD42020150373

Prefrontal cortical activation during working memory task anticipation contributes to discrimination between bipolar and unipolar depression.

Distinguishing bipolar disorder (BD) from major depressive disorder (MDD) is clinically challenging, especially during depressive episodes. While both groups are characterized by aberrant working memory and anticipatory processing, the role of these processes in discriminating BD from MDD remains unexplored. In this study, we examine how brain activation corresponding to anticipation of and performance on easy vs. difficult working memory tasks with emotional stimuli contributes to discrimination among BD, MDD, and healthy controls (HC). Depressed individuals with BD (n = 18), MDD (n = 23), and HC (n = 23) were scanned while performing a working memory task in which they had to first anticipate performance on 1-back (easy) or 2-back (difficult) tasks with happy, fearful, or neutral faces, and then, perform the task. Anticipation-related and task-related brain activation was measured in the whole brain using functional magnetic resonance imagining. We used an elastic-net regression for variable selection, and a random forest classifier for BD vs. MDD classification. The former selected the activation differences (1-back minus 2-back) in the lateral and medial prefrontal cortices (PFC) during task anticipation and performance on the working memory tasks with fearful and neutral faces as variables relevant for BD vs. MDD classification. BD vs. MDD were classified with 70.7% accuracy (p < 0.01) based on the neuroimaging measures alone, with 80.5% accuracy (p = 0.001) based on clinical measures alone, and with 85.4% accuracy (p < 0.001) based on clinical and neuroimaging measures together. These findings suggest that PFC activation during working memory task anticipation and performance may be an important biological marker distinguishing BD from MDD.

Sequential screening for depression in humanitarian emergencies: a validation study of the Patient Health Questionnaire among Syrian refugees

BackgroundDespite the need for mental health surveillance in humanitarian emergencies, there is a lack of validated instruments. This study evaluated a sequential screening process for major depressive disorder (MDD) using the two- and eight-item Patient Health Questionnaires (PHQ-2 and PHQ-8, respectively).MethodsThis study analyzed data collected during a cross-sectional survey in a Syrian refugee camp in Greece (n = 135). The response rate for each instrument was assessed, and response burden was calculated as the number of items completed. The sequential screening process was simulated to replicate the MDD classifications captured if the PHQ-2 was used to narrow the population receiving the full PHQ-8 assessment. All respondents were screened using the PHQ-2. Only respondents scoring ≥ 2 are considered at risk for symptoms of MDD and complete the remaining six items. The positive and negative percent agreement of this sequential screening process were evaluated.ResultsThe PHQ-2, PHQ-2/8 sequential screening process, and PHQ-8 were completed by 91%, 87%, and 84% of respondents, respectively. The sequential screening process had a positive percent agreement of 89% and a negative percent agreement of 100%, and eliminated the need to complete the full PHQ-8 scale for 34 (25%) respondents.ConclusionsThe benefits of the sequential screening approach for the classification of MDD presented here are twofold: preserving classification accuracy relative to the PHQ-2 alone while reducing the response burden of the PHQ-8. This sequential screening approach is a pragmatic strategy for streamlining MDD surveillance in humanitarian emergencies.