Accurate Diagnosis Of Depression Research Articles

Early Detection Depression Based On Action Unit and Eye Gaze Features Using a Multi-Input CNN-WoPL Framework

Depression is a common mental disorder with significant life impact, including a high risk of suicide. Patients with depression attempt suicide five times more often than the general population. Self-reporting, subjective judgement and clinician expertise influence conventional diagnostic methods. For timely intervention and effective treatment, early and accurate diagnosis of depression is essential. This study proposes a framework called Multi-Input CNN-WoPL, a CNN-based method without a pooling layer that combines two features - action units and gaze - to improve accuracy and robustness in automatic depression detection. Pooling layer reduces spatial dimension of feature map, resulting in loss of information related to expression data, affecting depression detection result. The performance of the proposed method results in an accuracy of 0.994 and F1 score = 0.993, the F1 score value close to 1.0 indicates that the proposed method has good precision, recall and performance.

Accurate detection depression cell model with a dual-locked fluorescence probe in response to noradrenaline and HClO

Due to the serious harm of depression to human health and quality of life, an accurate diagnosis of depression is warranted. For the complex etiology of depression, a single biomarker diagnostic method often leads to misdiagnosis. As noradrenaline and HClO are closely related to depression, a “dual-locked” fluorescence probe R-NE-HClO for diagnosing of depression through the simultaneous detection of noradrenaline and HClO was designed and synthesized. Fluorescence of R-NE-HClO can only be restored in the presence of both noradrenaline and HClO. The probe demonstrates excellent selectivity for noradrenaline and HClO and low cytotoxicity in cell imaging experiments. It is to be observed that we successfully applied the probe to accurately detect depressed cells which provides a possible tool for diagnosing depression.

Major depressive disorder: Brain signals as biomarkers for depression

Major depressive disorder: Brain signals as biomarkers for depression Tiago Costa from Delft University of Technology explores the potential of neuroimaging techniques, specifically non-invasive electroencephalography (EEG), in detecting biomarkers for depression. Major Depressive Disorder (MDD) is a prevalent and complex mental disorder posing significant challenges to both individuals and societies worldwide. An early and accurate diagnosis of depression is essential for effective treatment and management. In recent years, advancements in neuroimaging techniques have opened new avenues for identifying biomarkers that could aid in the diagnosis and understanding of depression. Among these techniques, non-invasive electroencephalography (EEG) has gained attention in the last decade for its potential to detect depression biomarkers. While there is significant heterogeneity in terms of methodologies, sample sizes, and research objectives, several consistent patterns and findings emerge.

AMGCN-L: an adaptive multi-time-window graph convolutional network with long-short-term memory for depression detection

Objective. Depression is a common chronic mental disorder characterized by high rates of prevalence, recurrence, suicide, and disability as well as heavy disease burden. An accurate diagnosis of depression is a prerequisite for treatment. However, existing questionnaire-based diagnostic methods are limited by the innate subjectivity of medical practitioners and subjects. In the search for a more objective diagnostic methods for depression, researchers have recently started to use deep learning approaches. Approach. In this work, a deep-learning network, named adaptively multi-time-window graph convolutional network (GCN) with long-short-term memory (LSTM) (i.e. AMGCN-L), is proposed. This network can automatically categorize depressed and non-depressed people by testing for the existence of inherent brain functional connectivity and spatiotemporal features contained in electroencephalogram (EEG) signals. AMGCN-L is mainly composed of two sub-networks: the first sub-network is an adaptive multi-time-window graph generation block with which adjacency matrices that contain brain functional connectivity on different time periods are adaptively designed. The second sub-network consists of GCN and LSTM, which are used to fully extract the innate spatial and temporal features of EEG signals, respectively. Main results. Two public datasets, namely the patient repository for EEG data and computational tools, and the multi-modal open dataset for mental-disorder analysis, were used to test the performance of the proposed network; the depression recognition accuracies achieved in both datasets (using tenfold cross-validation) were 90.38% and 90.57%, respectively. Significance. This work demonstrates that GCN and LSTM have eminent effects on spatial and temporal feature extraction, respectively, suggesting that the exploration of brain connectivity and the exploitation of spatiotemporal features benefit the detection of depression. Moreover, the proposed method provides effective support and supplement for the detection of clinical depression and later treatment procedures.

Functional role of frontal electroencephalogram alpha asymmetry in the resting state in patients with depression: A review.

Depression is a psychological disorder that affects the general public worldwide. It is particularly important to make an objective and accurate diagnosis of depression, and the measurement methods of brain activity have gradually received increasing attention. Resting electroencephalogram (EEG) alpha asymmetry in patients with depression shows changes in activation of the alpha frequency band of the left and right frontal cortices. In this paper, we review the findings of the relationship between frontal EEG alpha asymmetry in the resting state and depression. Based on worldwide studies, we found the following: (1) Compared with individuals without depression, those with depression showed greater right frontal EEG alpha asymmetry in the resting state. However, the pattern of frontal EEG alpha asymmetry in the resting state in depressive individuals seemed to disappear with age; (2) Compared with individuals without maternal depression, those with maternal depression showed greater right frontal EEG alpha asymmetry in the resting state, which indicated that genetic or experience-based influences have an impact on frontal EEG alpha asymmetry at rest; and (3) Frontal EEG alpha asymmetry in the resting state was stable, and little or no change occurred after antidepressant treatment. Finally, we concluded that the contrasting results may be due to differences in methodology, clinical characteristics, and participant characteristics.

A Resting-state Functional Magnetic Resonance Imaging Study of Whole-brain Functional Connectivity of Voxel Levels in Patients With Irritable Bowel Syndrome With Depressive Symptoms.

Background/AimsDepressive symptom is one of the most common symptoms in patients with irritable bowel syndrome (IBS), but its pathogenetic mechanisms remain unclear. As a voxel-level graph theory analysis method, degree centrality (DC) can provide a new perspective for exploring the abnormalities of whole-brain functional network of IBS with depressive symptoms (DEP-IBS).MethodsDC, voxel-wise image and clinical symptoms correlation and seed-based functional connectivity (FC) analyses were performed in 28 DEP-IBS patients, 21 IBS without depressive symptoms (nDEP-IBS) patients and 36 matched healthy controls (HC) to reveal the abnormalities of whole brain FC in DEP-IBS.ResultsCompared to nDEP-IBS patients and HC, DEP-IBS patients showed significant decrease of DC in the left insula and increase of DC in the left precentral gyrus. The DC’s z-scores of the left insula negatively correlated with depression severity in DEP-IBS patients. Compared to nDEP-IBS patients, DEP-IBS patients showed increased left insula-related FC in the left inferior parietal lobule and right inferior occipital gyrus, and decreased left insula-related FC in the left precentral gyrus, right supplementary motor area (SMA), and postcentral gyrus. In DEP-IBS patients, abstracted clusters’ mean FC in the right SMA negatively correlated with depressive symptoms.ConclusionsDEP-IBS patients have abnormal FC in brain regions associated with the fronto-limbic and sensorimotor networks, especially insula and SMA, which explains the vicious circle between negative emotion and gastrointestinal symptoms in IBS. Identification of such alterations may facilitate earlier and more accurate diagnosis of depression in IBS, and development of effective treatment strategies.

Brain structural and functional changes in patients with major depressive disorder: a literature review.

Depression is a mental disorder characterized by low mood and anhedonia that involves abnormalities in multiple brain regions and networks. Epidemiological studies demonstrated that depression has become one of the most important diseases affecting human health and longevity. The pathogenesis of the disease has not been fully elucidated. The clinical effect of treatment is not satisfactory in many cases. Neuroimaging studies have provided rich and valuable evidence that psychological symptoms and behavioral deficits in patients with depression are closely related to structural and functional abnormalities in specific areas of the brain. There were morphological differences in several brain regions, including the frontal lobe, temporal lobe, and limbic system, in people with depression compared to healthy people. In addition, people with depression also had abnormal functional connectivity to the default mode network, the central executive network, and the salience network. These findings provide an opportunity to re-understand the biological mechanisms of depression. In the future, magnetic resonance imaging (MRI) may serve as an important auxiliary tool for psychiatrists in the process of early and accurate diagnosis of depression and finding the appropriate treatment target for each patient to optimize clinical response.

Re-examining the robustness of voice features in predicting depression: Compared with baseline of confounders.

A large proportion of Depression Disorder patients do not receive an effective diagnosis, which makes it necessary to find a more objective assessment to facilitate a more rapid and accurate diagnosis of depression. Speech data is easy to acquire clinically, its association with depression has been studied, although the actual predictive effect of voice features has not been examined. Thus, we do not have a general understanding of the extent to which voice features contribute to the identification of depression. In this study, we investigated the significance of the association between voice features and depression using binary logistic regression, and the actual classification effect of voice features on depression was re-examined through classification modeling. Nearly 1000 Chinese females participated in this study. Several different datasets was included as test set. We found that 4 voice features (PC1, PC6, PC17, PC24, P<0.05, corrected) made significant contribution to depression, and that the contribution effect of the voice features alone reached 35.65% (Nagelkerke's R2). In classification modeling, voice data based model has consistently higher predicting accuracy(F-measure) than the baseline model of demographic data when tested on different datasets, even across different emotion context. F-measure of voice features alone reached 81%, consistent with existing data. These results demonstrate that voice features are effective in predicting depression and indicate that more sophisticated models based on voice features can be built to help in clinical diagnosis.

S13. DO PATIENTS WITH RECENT-ONSET DEPRESSION DIFFER CLINICALLY AND NEUROBIOLOGICALLY FROM DEPRESSED PATIENTS WITH A CLINICAL HIGH-RISK STATE FOR PSYCHOSIS?

BackgroundMajor depressive disorder (MDD) is one of the most common mental disorders, with a lifetime prevalence of 14.6%. The impact of depression is considerable; poor social and economic functioning and significant life limitations [1]. Depression is also the most common co-morbidity seen with other mental disorders. The prevalence of depressive disorder in schizophrenia has been reported to be around 40% [2]. When examining very early phases of illness, in groups identified as at clinical high risk (CHR) for psychosis, high rates of ‘co-morbid’ axis one diagnoses are reported, with over 50% reaching criteria for a depressive disorder. Those people with schizophrenia send depression are significantly more likely to relapse, to be a safety concern (be arrested, victimized or suicidal), have greater substance-related problems and poorer recovery [2]. In addition, depression has been linked to increased risk of transition from CHR to FEP, suggesting that in this group depression also indicates a poorer outcome [3]. Currently, the diagnosis of depression is based on the phenomenological evaluation of symptoms and behavior. However, there remains significant debate around the heterogeneity of depressive symptoms and their function as prognostic indicators [4]. Neuroimaging holds “diagnostic potential” for depression [5]. However, studies show that brain alterations are often small and reliability is difficult, and there has been no neuroimaging investigation of depression as a co-morbid diagnosis. We aim to further understand the symptom profile of depression in emerging mental disorders, including in the clinical high risk group (CHR) and recent onset psychosis (ROP) as compared to those with recent onset depression (ROD). This has important implications for the accurate identification of a potentially malleable target for treatment, and indeed development of novel therapeutic options. We also aim to explore the ability of brain imagining (structural MRI) to add accuracy to the classification prediction modelsMethodsData from the PRONIA study, an EUFP7 funded 8 center study recruiting ROD, CHR and ROP participants will be presented. Analysis will include demographic information and BDI-II (Beck Depression Inventory), CAARMS (Comprehensive Assessment of the At Risk Mental State), SANS (Scale for the assessment of negative symptoms) total score PANSS (Positive and negative Symptom Score) and SPI-A together with structural MRI imaging. We will report descriptive detail from the PRONIA discovery sample (n716), machine learning classification with Neurominer® and VBM analysis of sMRI scans across groups.ResultsData from BDI-II symptom endorsement suggests a ‘classical depression phenotype’ corresponding to Becks ‘cognitive triad’; “life is pointless, future hopeless, self as worthless” may separate depression in ROD from ROP, with other symptoms potentially able to separate ROP from ROD. In classification, a 65% sensitivity and specificity are found. Data will also be presented on the CHR group and their alignment, together with VBM analysis for structural MRI examining correlates with highly weighted classifying symptoms in and across all three groups.DiscussionWhen given early in the course of illness, interventions have the greatest potential impact, and characterization and accurate diagnosis of depression in emerging mental disorders is an important goal. This study suggests it may be possible to accurately identify depression in different diagnostic categories, including major depressive disorder, psychosis and clinical high risk, and that neuroimaging holds potential to add to diagnostic accuracy in complex co-morbid disorders.

Presentation and Validation of the Multiple Sclerosis Depression Rating Scale: A Test Specifically Devised to Investigate Affective Disorders in Multiple Sclerosis Patients

Accurate diagnosis of depression in patients affected by MS is important, as it may be a cause of reduced quality of life and increased suicide risk. We present a new scale, the Multiple Sclerosis Depression Rating Scale (MSDRS), and assess its diagnostic accuracy in comparison to the Beck Depression Inventory (BDI). A total of 94 MS participants were classified as non-depressed (N = 44) or affected by mood disorder associated to MS with depressive manifestations (MSD-MDDM; N = 37) or with a major depression-like episode (MSD-MDL; N = 13). Each participant underwent a psychiatric interview, MSDRS, and BDI; diagnostic accuracy was evaluated using area under the ROC curve (AROC). The diagnostic accuracy of MSDRS and BDI was comparable when diagnosing both MSD-MDDM and MSD-MDL (AROC respectively 0.8998 and 0.8659); the MSDRS showed higher accuracy for the diagnosis of MSD-MDL (AROC respectively 0.9278 and 0.8314; p = .038). The MSDRS may be a reliable tool for the diagnosis of depression in MS.

Detection of bipolar disorder

Major depressive episodes are common in bipolar disorder, which consequently may be misdiagnosed as major depressive disorder. Improved detection of bipolar disorder rests upon better ascertainment of a history of hypomania. Antidepressants are of dubious benefit in bipolar disorder and more accurate diagnosis of depression would promote better treatment.

The Role of Depression in Predicting Antiretroviral Adherence in Ugandan Parents and Their Children Initiating HAART in the MTCT-Plus Family Treatment Model: Commentary on Jayne Byakika-Tusiime et al. 2009

This commentary discusses a study that investigates the association between depression and adherence to antiretroviral therapy (ART) for HIV positive patients in Uganda. It does however address methodological issues from the study that are important to the translation of the results such as: a lack of cultural adaption of the Becks depression inventory (BDI) the accurate diagnosis of depression and the misclassification of ART patients.

Translation and validation of brief patient health questionnaire against DSM IV as a tool to diagnose major depressive disorder in Indian patients

Depression is frequently encountered in the primary care setting but is often unrecognized and hence untreated. There is a need for a uniform user-friendly screening instrument for depression for primary healthcare personnel in India. Translation and validation of the brief patient health questionnaire (BPHQ) as a screening tool for depression in major Indian languages. This was a prospective study conducted at 18 sites, in psychiatric and general clinics. The English version of the BPHQ was translated into 11 Indian languages. The translations were reviewed by experts and volunteers and proofread for the final translated BPHQ. The validation exercise included more than 3000 subjects. A psychiatrist and a psychiatry social worker / coordinator conducted the study under the supervision of the principal investigator. For each language, the presence or absence of major depressive disorder (MDD) as diagnosed with the help of a patient-completed BPHQ and the psychiatrist DSM-IV diagnosis was matched. The kappa coefficient was used as a measure of inter-observer agreement between the two diagnostic methods. Seven languages failed the primary validation exercise. These translations were reviewed and the updated versions, after proofreading were re-run for validation. The self-administered BPHQ was successfully translated and validated for diagnosis of MDD against DSM-IV diagnosis made by a psychiatrist, in English, Hindi, Marathi oriya, Malayalam, Assamese, Gujarati, Kannada, Telugu, Bengali and Tamil. BPHQ is a simple, quick and reliable instrument, which facilitates rapid and accurate diagnosis of depression in the primary care setting in our country.

The problem of diagnosing major depression in elderly primary care patients

Background: To clarify the problem of diagnosing major depression in elderly primary care patients, we studied the nuances of diagnostic classification by general practitioners (GPs) and the relationship between sociodemographic and clinical factors and an accurate diagnosis of depression. Methods: As part of a national survey of general practice a standardised psychiatric interview (CIDI) was performed in 237 subjects ≥55 years screened for the presence of psychopathology. Fifty-five patients were found to suffer from a major depressive disorder in the last 12 months. In these patients, GPs registered during 1 year all contact diagnoses and prescriptions of medication. Results: Nearly all depressed patients (96.4%) had one or more contacts with their GP during 1 year. GPs classified 20.8% of the patients as having a down/depressed feeling or depression, while 32.1% as having other psychological problems than depression. It was remarkable that an accurate diagnosis by GPs was significantly related to higher age in this age group. Regarding the clinical characteristics, there was a significantly higher number of prescriptions of antidepressants in the accurately diagnosed patients. We found no significant differences in respect to other clinical characteristics (e.g. severity and number of symptoms, comorbidity of anxiety and somatic disorders). Conclusions: GPs are aware of the psychological problems in half of the elderly patients with major depression, but do not explicitly distinguish depressive symptoms from other psychological problems or from social problems. Integrated programs may be more promising to improve the diagnostic rate than clinical education or guideline implementation alone.

The diagnosis and treatment of depression in dementia.

For many years, the problem of accurate diagnosis of depression among elderly patients with cognitive impairment has focused on patients with major depression who are misdiagnosed as having dementia—cases of so-called depressive pseudodementia. Yet the more common, and possibly more difficult, predicament for the clinician is making the diagnosis of depression for the patient with established dementia. This difficulty is often exacerbated when a diagnosis is needed for an institutionalized patient with severe dementia, yet reliable information about subjective affective symptoms is lacking due to the patient’s cognitive status. This paper discusses ways to improve diagnostic accuracy in treating patients with depression and dementia and reviews treatment options for these patients. Diagnostic issues

'Bridge' between knowledge and practice in diagnosing depression.

Depression is one of the most common disorders in primary care patients. Yet, physicians fail to recognize it in most patients. One of the likely reasons for this problem may be that medical students and physicians have difficulty recalling pertinent psychiatric didactic information in a clinically usable form. To that end, the author presents three mnemonics as a means to help clinicians easily recall the information important to making an accurate diagnosis of depression in their patients.

Are All Recently Detoxified Alcoholics Depressed?

Reports of the prevalence of depression among alcoholics vary from 3% to 98%; the authors attribute this variation to the use of different diagnostic criteria. They used clinical diagnosis, the Hamilton Depression Rating Scale, the Zung Self-Rating Depression Scale, and the MMPI to diagnose 35 men recently withdrawn from alcohol. The percentages of depression diagnosed were 8.6%, 28%, 66%, and 43%, respectively. The authors point out that the Hamilton, Zung, and MMPI scales are not diagnostic of depression in themselves and that accurate diagnosis of depression in alcoholics will reduce inappropriate treatment of nondepressed alcoholics and ensure careful treatment of the truly depressed.