Heterogeneity Of Major Depressive Disorder Research Articles

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework.

Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people’s quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients’ brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

Youth depression and inflammation: Cross-sectional network analyses of C-Reactive protein, interleukin-6 and symptoms in a population-based sample

BackgroundInflammation-related proteins constitute a promising avenue in studying biological correlates of major depressive disorder (MDD). However, MDD is a heterogeneous condition – a crucial aspect to be considered in association studies. We examined whether inflammatory proteins are associated with categorical diagnosis, a dimensional total sum-score, and specific depressive symptoms among youths. MethodsWe analyzed data from the 1993 Pelotas Birth Cohort, a population-based study in Brazil that followed individuals up to age 22 years. Categorical psychiatric diagnoses were derived using adapted modules of the Mini International Neuropsychiatric Interview (MINI). Dimensional symptomatology was assessed using the Brazilian Portuguese version of the Center for Epidemiological Studies–Depression Scale–Revised (CESD-R). We estimated network structures that included individual depressive symptoms as measured by CESD-R items, peripheral inflammatory markers (C-Reactive Protein [CRP] and Interleukin-6 [IL-6]), as well as relevant covariates. ResultsWe evaluated 2586 participants (mean age = 22.5[SD = 0.33]) There were no associations between concentrations of inflammatory proteins and categorical diagnosis of MDD or with CESD-R total sum-scores. In symptom-specific analysis, CRP and IL-6 were positively connected to somatic and cognitive items. DiscussionWe found cross-sectional connections of two commonly studied inflammatory proteins and specific depressive symptoms. Conducting symptom-specific analyses in relation to biological markers might advance our understanding of the heterogeneity of MDD.

Improving the diagnostic accuracy for major depressive disorder using machine learning algorithms integrating clinical and near-infrared spectroscopy data

BackgroundGiven that major depressive disorder (MDD) is both biologically and clinically heterogeneous, a diagnostic system integrating neurobiological markers and clinical characteristics would allow for better diagnostic accuracy and, consequently, treatment efficacy. ObjectiveOur study aimed to evaluate the discriminative and predictive ability of unimodal, bimodal, and multimodal approaches in a total of seven machine learning (ML) models—clinical, demographic, functional near-infrared spectroscopy (fNIRS), combinations of two unimodal models, as well as a combination of all three—for MDD. MethodsWe recruited 65 adults with MDD and 68 matched healthy controls, who provided both sociodemographic and clinical information, and completed the HAM-D questionnaire. They were also subject to fNIRS measurement when participating in the verbal fluency task. Using the nested cross validation procedure, the classification performance of each ML model was evaluated based on the area under the receiver operating characteristic curve (ROC), balanced accuracy, sensitivity, and specificity. ResultsThe multimodal ML model was able to distinguish between depressed patients and healthy controls with the highest balanced accuracy of 87.98 ± 8.84% (AUC = 0.92; 95% CI (0.84–0.99) when compared with the uni- and bi-modal models. ConclusionsOur multimodal ML model demonstrated the highest diagnostic accuracy for MDD. This reinforces the biological and clinical heterogeneity of MDD and highlights the potential of this model to improve MDD diagnosis rates. Furthermore, this model is cost-effective and clinically applicable enough to be established as a robust diagnostic system for MDD based on patients’ biosignatures.

Genetic dissection of the clinical heterogeneity of major depressive disorder

Genetic dissection of the clinical heterogeneity of major depressive disorder

Data-driven clustering differentiates subtypes of major depressive disorder with distinct brain connectivity and symptom features.

Major depressive disorder (MDD) is a clinically and biologically heterogeneous syndrome. Identifying discrete subtypes of illness with distinguishing neurobiological substrates and clinical features is a promising strategy for guiding personalised therapeutics. This study aimed to identify depression subtypes with correlated patterns of functional network connectivity and clinical symptoms by clustering patients according to a weighted linear combination of both features in a relatively large, medication-naïve depression sample. We recruited 115 medication-naïve adults with MDD and 129 matched healthy controls, and evaluated all participants with magnetic resonance imaging. We used regularised canonical correlation analysis to identify component mapping relationships between functional network connectivity and symptom profiles, and K-means clustering was used to define distinct subtypes of patients. Two subtypes of MDD were identified: insomnia-dominated subtype 1 and anhedonia-dominated subtype 2. Subtype 1 was characterised by abnormal hyperconnectivity within the ventral attention network and sleep maintenance insomnia. Subtype 2 was characterised by abnormal hypoconnectivity in the subcortical and dorsal attention networks, and prominent anhedonia symptoms. Our study identified two distinct subtypes of patients with specific neurobiological and clinical symptom profiles. These findings advance understanding of the biological and clinical heterogeneity of MDD, offering a pathway for defining categorical subtypes of illness via consideration of both biological and clinical features.

Improving cognitive functioning in major depressive disorder with psychedelics: A dimensional approach

The high symptomatic and biological heterogeneity of major depressive disorder (MDD) makes it very difficult to find broadly efficacious treatments that work against all symptoms. Concentrating on single core symptoms that are biologically well understood might consist of a more viable approach. The Research Domain Criteria (RDoC) framework is a trans-diagnostic dimensional approach that focuses on symptoms and their underlying neurobiology. Evidence is accumulating that psychedelics may possess antidepressant activity, and this can potentially be explained through a multi-level (psychobiological, circuitry, (sub)cellular and molecular) analysis of the cognitive systems RDoC domain. Cognitive deficits, such as negative emotional processing and negativity bias, often lead to depressive rumination. Psychedelics can increase long-term cognitive flexibility, leading to normalization of negativity bias and reduction in rumination. We propose a theoretical model that explains how psychedelics can reduce the negativity bias in depressed patients. At the psychobiological level, we hypothesize that the negativity bias in MDD is due to impaired pattern separation and that psychedelics such as psilocybin help in depression because they enhance pattern separation and hence reduce negativity bias. Pattern separation is a mnemonic process that relies on adult hippocampal neurogenesis, where similar inputs are made more distinct, which is essential for optimal encoding of contextual information. Impairment in this process may underlie the negative cognitive bias in MDD by, for example, increased pattern separation of cues with a negative valence that can lead to excessive deliberation on aversive outcomes. On the (sub) cellular level, psychedelics stimulate hippocampal neurogenesis as well as synaptogenesis, spinogenesis and dendritogenesis in the prefrontal cortex. Together, these effects help restoring resilience to chronic stress and lead to modulation of the major connectivity hubs of the prefrontal cortex, hippocampus, and amygdala. Based on these observations, we propose a new translational framework to guide the development of a novel generation of therapeutics to treat the cognitive symptoms in MDD.

Identifying Subgroups of Major Depressive Disorder Using Brain Structural Covariance Networks and Mapping of Associated Clinical and Cognitive Variables

BackgroundIdentifying data-driven subtypes of major depressive disorder (MDD) holds promise for parsing the heterogeneity of MDD in a neurobiologically informed way. However, limited studies have used brain structural covariance networks (SCNs) for subtyping MDD. MethodsThis study included 145 unmedicated patients with MDD and 206 demographically matched healthy control subjects, who underwent a structural magnetic resonance imaging scan and a comprehensive neurocognitive battery. Patterns of structural covariance were identified using source-based morphometry across both patients with MDD and healthy control subjects. K-means clustering algorithms were applied on dysregulated structural networks in MDD to identify potential MDD subtypes. Finally, clinical and neurocognitive measures were compared between identified subgroups to elucidate the profile of these MDD subtypes. ResultsSource-based morphometry across all individuals identified 28 whole-brain SCNs that encompassed the prefrontal, anterior cingulate, and orbitofrontal cortices; basal ganglia; and cerebellar, visual, and motor regions. Compared with healthy control subjects, individuals with MDD showed lower structural network integrity in three networks including default mode, ventromedial prefrontal cortical, and salience networks. Clustering analysis revealed two MDD subtypes based on the patterns of structural network abnormalities in these three networks. Further profiling revealed that patients in subtype 1 had younger age of onset and more symptom severity as well as greater deficits in cognitive performance than patients in subtype 2. ConclusionsOverall, we identified two MDD subtypes based on SCNs that differed in their clinical and cognitive profile. Our results represent a proof-of-concept framework for leveraging these large-scale SCNs to parse heterogeneity in MDD.

Obesity and atypical depression symptoms: findings from Mendelian randomization in two European cohorts

Studies considering the causal role of body mass index (BMI) for the predisposition of major depressive disorder (MDD) based on a Mendelian Randomization (MR) approach have shown contradictory results. These inconsistent findings may be attributable to the heterogeneity of MDD; in fact, several studies have documented associations between BMI and mainly the atypical subtype of MDD. Using a MR approach, we investigated the potential causal role of obesity in both the atypical subtype and its five specific symptoms assessed according to the Statistical Manual of Mental Disorders (DSM), in two large European cohorts, CoLaus|PsyCoLaus (n = 3350, 1461 cases and 1889 controls) and NESDA|NTR (n = 4139, 1182 cases and 2957 controls). We first tested general obesity measured by BMI and then the body fat distribution measured by waist-to-hip ratio (WHR). Results suggested that BMI is potentially causally related to the symptom increase in appetite, for which inverse variance weighted, simple median and weighted median MR regression estimated slopes were 0.68 (SE = 0.23, p = 0.004), 0.77 (SE = 0.37, p = 0.036), and 1.11 (SE = 0.39, p = 0.004). No causal effect of BMI or WHR was found on the risk of the atypical subtype or for any of the other atypical symptoms. Our findings show that higher obesity is likely causal for the specific symptom of increase in appetite in depressed participants and reiterate the need to study depression at the granular level of its symptoms to further elucidate potential causal relationships and gain additional insight into its biological underpinnings.

Distinct epigenetic signatures between adult-onset and late-onset depression

The heterogeneity of major depressive disorder (MDD) is attributed to the fact that diagnostic criteria (e.g., DSM-5) are only based on clinical symptoms. The discovery of blood biomarkers has the potential to change the diagnosis of MDD. The purpose of this study was to identify blood biomarkers of DNA methylation by strategically subtyping patients with MDD by onset age. We analyzed genome-wide DNA methylation of patients with adult-onset depression (AOD; age ≥ 50 years, age at depression onset < 50 years; N = 10) and late-onset depression (LOD; age ≥ 50 years, age at depression onset ≥ 50 years; N = 25) in comparison to that of 30 healthy subjects. The methylation profile of the AOD group was not only different from that of the LOD group but also more homogenous. Six identified methylation CpG sites were validated by pyrosequencing and amplicon bisulfite sequencing as potential markers for AOD in a second set of independent patients with AOD and healthy control subjects (N = 11). The combination of three specific methylation markers achieved the highest accuracy (sensitivity, 64%; specificity, 91%; accuracy, 77%). Taken together, our findings suggest that DNA methylation markers are more suitable for AOD than for LOD patients.

Challenges and Strategies for Current Classifications of Depressive Disorders: Proposal for Future Diagnostic Standards.

The Diagnostic and Statistical Manual of Mental Disorder, Fourth Edition (DSM-IV) was revised based on a combination of a categorical and a dimensional approach such that in the DSM, Fifth Edition (DSM-5), depressive disorders have been separated as a distinctive disease entity from bipolar disorders, consistent with the deconstruction of Kraepelinian dualism. Additionally, the diagnostic thresholds of depressive disorders may be reduced due to the addition of "hopelessness" to the subjective descriptors of depressed mood and the removal of the "bereavement exclusion." Manic/hypomanic, psychotic, and anxious symptoms in major depressive disorder (MDD) and other depressive disorders are described using the transdiagnostic specifiers of "with mixed features," "with psychotic features," and "with anxious distress," respectively. Additionally, due to the polythetic and operational characteristics of the DSM-5 diagnostic criteria, the heterogeneity of MDD is inevitable. Thus, 227 different symptom combinations fulfill the DSM-5 diagnostic criteria for MDD. This heterogeneity of MDD is criticized in view of the Wittgensteinian analogy of language game. Depression subtypes determined by disturbances in monoamine levels and the severity of the disease have been identified in the literature. According to a review of the Gottesman and Gould criteria, neuroticism, morning cortisol, cortisol awakening response, asymmetry in frontal cortical activity on electroencephalography (EEG), and probabilistic reward learning, among other variables, are evidenced as endophenotypes for depressive disorders. Network analysis has been proposed as a potential method to compliment the limitations of current diagnostic criteria and to explore the pathways between depressive symptoms, as well as to identify novel and interesting relationships between depressive symptoms. Based on the literature on network analysis in this field, no differences in the centrality index of the DSM and non-DSM symptoms were repeatedly present among patients with MDD. Furthermore, MDD and other depressive syndromes include two of the Research Domain Criteria (RDoC), including the Loss construct within the Negative Valence Systems domains and various Reward constructs within the Positive Valence Systems domain.

Personality Disturbances in Melancholic and Nonmelancholic Unipolar Major Depression: A Systematic Review and Meta-Analysis.

Although melancholic depression has been associated with a more adequate premorbid personality style, the empirical evidence supporting this statement is inconclusive. We conducted a systematic review and meta-analyzed studies comparing the presence of personality disturbances in melancholic and nonmelancholic subtypes of major depressive disorder (MDD). We defined a) a continuous outcome, defining personality traits as a dimensional construct, and b) a dichotomous outcome, defined as the presence/absence of personality disorders (PD). We also evaluated the role of potential moderators. Our results showed significantly higher levels of neuroticism and interpersonal sensitivity, and a higher likelihood of presenting a PD in nonmelancholic depression. No significant differences were found for extraversion. The scarcity of studies and high heterogeneity were among our limitations. In conclusion, personality disturbances seem to be overrepresented in nonmelancholic MDD. The assessment of personality disturbances can be useful in clinical practice and in the study of MDD heterogeneity.

Subtypes of Depression: Latent Class Analysis in Spanish Old People with Depressive Symptoms.

Major depressive disorder (MDD) is one of the most disabling disorders and the one that most contributes to disability. When it occurs in older people, it is an additional burden to their potential physical and cognitive deficiencies, making MDD an important public health problem that supposes a large investment in health. There is a clear lack of consistency between the subtypes of depression found in the literature, ranging from two to seven classes, with three being the most commonly found non-melancholic, melancholic and psychotic, or putative psychotics. The aim of this research is to add knowledge to the profiles of depressive symptoms in a representative sample of older Spanish people, and to study the possible relationship of these symptom profiles with variables that have traditionally been related to depression. Spanish data from the sixth wave of SHARE were used, with 612 Spanish older adults living in Spain. A routine of several LCAs with a different number of classes was performed to answer this first aim to classify Spanish adults with depression symptoms. The results pointed out the presence of three different classes among the participants in the study: psychosomatic (11.12%), melancholic (14.21%), and anhedonic (74.67%). This work represents a step forward to understand the heterogeneity of major depressive disorder, facilitating the diagnosis, and subsequent treatment of older adults.

Positive and negative valence systems in major depression have distinct clinical features, response to antidepressants, and relationships with immunomarkers.

Heterogeneity in major depressive disorder (MDD) is well recognized but not well understood. Core depressive features are reward and emotional symptoms, which reflect dysfunctions in the positive valence (PV) and negative valence (NV) systems, respectively. This study assessed whether PV and NV systems (based on selected symptoms) were associated with different clinical features, antidepressant response, and levels of immunomarkers in adults with MDD. These analyses used data from combining medications to enhance depression outcomes study (N = 665; n = 166 for immunomarkers). PV and NV symptom scores were extracted from the clinician-rated 30-item Inventory of Depressive Symptomatology. Correlational analyses were conducted. PV and NV symptom scores were substantially associated with different clinical features. PV symptoms (impaired motivation, impaired energy, and anhedonia) were independently associated with female gender (p < .001), older age (p = .012), and higher cognitive and physical impairment (p < .001) according to the 7-item Cognitive and Physical Functioning Questionnaire. Conversely, NV symptoms (anxiety and interpersonal sensitivity) were independently associated with younger age (p = .013), more anxious comorbidities (p = .001 for generalized anxiety disorder and p = .002 for social phobia) and other commonly associated noncriterion symptoms (p < .001). Overall, PV symptoms were more responsive to antidepressants than NV symptoms (p < .0001; Cohen's d = .455). A PV symptom score was positively correlated with the concentration of three proinflammatory and one anti-inflammatory factor. In contrast, an NV symptom score was negatively associated with only one proinflammatory immunomarker. PV and NV systemfunctions appearto be reflected in selected clinical symptoms that differentially relate to other clinical features, treatment outcomes, and immunological function.

An Overview of the Heterogeneity of Major Depressive Disorder: Current Knowledge and Future Prospective.

Major depressive disorder (MDD) is estimated to impose maximum debilitating effects on the society by 2030, with its critical effects on health, functioning, quality of life and concomitant high levels of morbidity and mortality. Yet, the disease is inadequately understood, diagnosed and treated. Moreover, with the recent drastic rise in the pace of life, stress has materialized as one of the most potent environmental factors for depression. In this scenario, it is important to understand the modern pathogenetic hypotheses and mechanisms, and possibly try to shift from the traditional approaches in depression therapy. These include the elaboration of pathophysiological changes in heterogeneous systems such as genetic, epigenetic, serotonergic, noradrenergic, gamma-aminobutyric acid, glutamatergic and endocannabinoid systems, neurotrophic factors, HPA axis, immune system as well as cellular stress mechanisms. These components interact with each other in a complex matrix and further elucidation of their mechanism and cascade pathways are needed. This might aid in the identification of MDD subtypes as well as the development of sophisticated biomarkers. Further, characterization might also aid in developing multitargeted therapies that hold much promise as compared to the conventional monoamine based treatment. New candidate pharmacons, refined psychotherapeutic modalities, advanced neuro-surgical and imaging techniques as well as the implementation of pharmacokinetic, pharmacogenetic prescribing guidelines constitute the emerging expanses of MDD treatment.

Downregulated transferrin receptor in the blood predicts recurrent MDD in the elderly cohort: A fuzzy forests approach

BackgroundAt present, no predictive markers for Major Depressive Disorder (MDD) exist. The search for such markers has been challenging due to clinical and molecular heterogeneity of MDD, the lack of statistical power in studies and suboptimal statistical tools applied to multidimensional data. Machine learning is a powerful approach to mitigate some of these limitations. MethodsWe aimed to identify the predictive markers of recurrent MDD in the elderly using peripheral whole blood from the Sydney Memory and Aging Study (SMAS) (N = 521, aged over 65) and adopting machine learning methodology on transcriptome data. Fuzzy Forests is a Random Forests-based classification algorithm that takes advantage of the co-expression network structure between genes; it allows to alleviate the problem of p >> n via reducing the dimensionality of transcriptomic feature space. ResultsBy adopting Fuzzy Forests on transcriptome data, we found that the downregulated TFRC (transferrin receptor) can predict recurrent MDD with an accuracy of 63%. LimitationsAlthough we corrected our data for several important confounders, we were not able to account for the comorbidities and medication taken, which may be numerous in the elderly and might have affected the levels of gene transcription. ConclusionsWe found that downregulated TFRC is predictive of recurrent MDD, which is consistent with the previous literature, indicating the role of the innate immune system in depression. This study is the first to successfully apply Fuzzy Forests methodology on psychiatric condition, opening, therefore, a methodological avenue that can lead to clinically useful predictive markers of complex traits.

Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes

BackgroundAcylcarnitines have important functions in mitochondrial energetics and β-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD): core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+). MethodsDepressed outpatients (n = 240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and after eight weeks of treatment with citalopram or escitalopram were profiled for short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ®p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminated the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differed between groups. ResultsCompared to ANX+, CD+ and NVSM+ had significantly lower concentrations of short- and long-chain acylcarnitines at both baseline and week 8. In NVSM+, the medium- and long-chain acylcarnitines were also significantly lower in NVSM+ compared to ANX+. Short-chain acylcarnitine levels increased significantly from baseline to week 8 in CD+ and ANX+, whereas medium- and long-chain acylcarnitines significantly decreased in CD+ and NVSM+. ConclusionsIn depressed patients treated with SSRIs, β-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.

Unraveling the genetic architecture of major depressive disorder: merits and pitfalls of the approaches used in genome-wide association studies.

To identify genetic risk loci for major depressive disorder (MDD), two broad study design approaches have been applied: (1) to maximize sample size by combining data from different phenotype assessment modalities (e.g. clinical interview, self-report questionnaires) and (2) to reduce phenotypic heterogeneity through selecting more homogenous MDD subtypes. The value of these strategies has been debated. In this review, we summarize the most recent findings of large genomic studies that applied these approaches, and we highlight the merits and pitfalls of both approaches with particular attention to methodological and psychometric issues. We also discuss the results of analyses that investigated the heterogeneity of MDD. We conclude that both study designs are essential for further research. So far, increasing sample size has led to the identification of a relatively high number of genomic loci linked to depression. However, part of the identified variants may be related to a phenotype common to internalizing disorders and related traits. As such, samples containing detailed clinical information are needed to dissect depression heterogeneity and enable the potential identification of variants specific to a more restricted MDD phenotype. A balanced portfolio reconciling both study design approaches is the optimal approach to progress further in unraveling the genetic architecture of depression.

Diagnostic Issues of Depressive Disorders from Kraepelinian Dualism to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition.

Because the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) was mainly influenced by the neo-Kraepelinian approach, its categorical approach to defining mental disorders has been criticized from the viewpoint of etiological neutrality. In the context of bridging the gap between “presumed etiologies-based symptomatology” and “identifiable pathophysiological etiologies,” the content in 5th edition, the DSM-5, has been revised to incorporate a combination of categorical and dimensional approaches. The most remarkable change of note regarding the diagnostic classification of depressive disorders in the DSM-5 is the splitting of mood disorders into bipolar disorders and depressive disorders, which is in accordance with the deconstruction of the Kraepelinian dualism for psychoses. The transdiagnostic specifiers “with mixed features,” “with psychotic features,” and “with anxious distress” are introduced to describe the relationships of depressive disorders with bipolar disorders, schizophrenia, and generalized anxiety disorder, respectively, in a dimensional manner. The lowering of the diagnostic threshold for major depressive disorder (MDD) may be caused by the addition of “hopelessness” to the subjective descriptors of depressive mood and the elimination of “bereavement exclusion” from the definition of MDD. Since the heterogeneity of MDD is equivalent to the Wittgensteinian “games” analogy, the different types of MDD are related not by a single essential feature but rather by “family resemblance.” Network analyses of MDD symptoms may therefore need further review to elucidate the connections among interrelated symptoms and other clinical elements.

Depressive Symptom Dimensions in Treatment-Resistant Major Depression and Their Modulation With Electroconvulsive Therapy

Symptom heterogeneity in major depressive disorder obscures diagnostic and treatment-responsive biomarker identification. Whether symptom constellations are differentially changed by electroconvulsive therapy (ECT) remains unknown. We investigate the clustering of depressive symptoms over the ECT index and whether ECT differentially influences symptom clusters. The 17-item Hamilton Depression Rating Scale (HDRS-17) was collected from 111 patients with current depressive episode before and after ECT from 4 independent participating sites of the Global ECT-MRI Research Collaboration. Exploratory factor analysis of HDRS-17 items pre- and post-ECT treatment identified depressive symptom dimensions before and after ECT. A 2-way analysis of covariance was used to determine whether baseline symptom clusters were differentially changed by ECT between treatment remitters (defined as patients with posttreatment HDRS-17 total score ≤8) and nonremitters while controlling for pulse width, titration method, concurrent antidepressant treatment, use of benzodiazepine, and demographic variables. A 3-factor solution grouped pretreatment HDRS-17 items into core mood/anhedonia, somatic, and insomnia dimensions. A 2-factor solution best described the symptoms at posttreatment despite poorer separation of items. Among remitters, core mood/anhedonia symptoms were significantly more reduced than somatic and insomnia dimensions. No differences in symptom dimension trajectories were observed among nonremitting patients. Electroconvulsive therapy targets the underlying source of depressive symptomatology and may confer differential degrees of improvement in certain core depressive symptoms. Our findings of differential trajectories of symptom clusters over the ECT index might help related predictive biomarker studies to refine their approaches by identifying predictors of change along each latent symptom dimension.

Stratifying major depressive disorder by polygenic risk for schizophrenia in relation to structural brain measures.

Substantial clinical heterogeneity of major depressive disorder (MDD) suggests it may group together individuals with diverse aetiologies. Identifying distinct subtypes should lead to more effective diagnosis and treatment, while providing more useful targets for further research. Genetic and clinical overlap between MDD and schizophrenia (SCZ) suggests an MDD subtype may share underlying mechanisms with SCZ. The present study investigated whether a neurobiologically distinct subtype of MDD could be identified by SCZ polygenic risk score (PRS). We explored interactive effects between SCZ PRS and MDD case/control status on a range of cortical, subcortical and white matter metrics among 2370 male and 2574 female UK Biobank participants. There was a significant SCZ PRS by MDD interaction for rostral anterior cingulate cortex (RACC) thickness (β = 0.191, q = 0.043). This was driven by a positive association between SCZ PRS and RACC thickness among MDD cases (β = 0.098, p = 0.026), compared to a negative association among controls (β = -0.087, p = 0.002). MDD cases with low SCZ PRS showed thinner RACC, although the opposite difference for high-SCZ-PRS cases was not significant. There were nominal interactions for other brain metrics, but none remained significant after correcting for multiple comparisons. Our significant results indicate that MDD case-control differences in RACC thickness vary as a function of SCZ PRS. Although this was not the case for most other brain measures assessed, our specific findings still provide some further evidence that MDD in the presence of high genetic risk for SCZ is subtly neurobiologically distinct from MDD in general.