Data-driven biological subtypes of depression: systematic review of biological approaches to depression subtyping

Individualized functional connectome abnormalities obtained using two normative model unveil neurophysiological subtypes of obsessive compulsive disorder

The high heterogeneity observed among patients with obsessive-compulsive disorder (OCD) underscores the need to identify neurophysiological OCD subtypes to facilitate personalized diagnosis and treatment. In this study, our aim was to identify potential OCD subtypes based on individualized functional connectome abnormalities. We recruited a total of 99 patients with OCD and 104 healthy controls (HCs) matched for demographic characteristics. Individualized functional connectome abnormalities were obtained using normative models of functional connectivity strength (FCS) and used as features to unveil OCD subtypes. Sensitivity analyses were conducted to assess the reproducibility and robustness of the clustering outcomes. Patients exhibited significant intersubject heterogeneity in individualized functional connectome abnormalities. Two subtypes with distinct patterns of FCS abnormalities relative to HCs were identified. Subtype 1 patients primarily exhibited significantly decreased FCS in regions including the frontal gyrus, insula, hippocampus, and precentral/postcentral gyrus, whereas subtype 2 patients demonstrated increased FCS in widespread brain regions. When all patients were combined, no significant differences were observed. Additionally, the identified subtypes showed significant differences in age of onset. Furthermore, sensitivity analyses confirmed the reproducibility of our subtyping results. In conclusion, the identified OCD subtypes shed new light on the taxonomy and neurophysiological heterogeneity of OCD.

Depression symptoms are associated with demographic characteristics, nutritional status, and social support among young adults in Chile: a latent class analysis

BACKGROUND

Depressive disorders are a critical public health concern in Chile. Nonetheless, there is a lack of evidence regarding the identification of depressive symptom clusters. The objective was to identify depressive symptom clusters among Chilean young adults and examine how demographic, and lifestyle factors as well as social support can influence and predict them.

METHODS

Cross-sectional study conducted among 1,000 participants from the Limache cohort 2. A latent class analysis (LCA) was performed to identify depressive symptom clusters, using the Patient Health Questionnaire (PHQ-9). Multinomial logistic regression was then applied to explore the associations between identified classes and potential predictors. The models were adjusted by age and sex.

RESULTS

Three latent classes of depressive symptoms were identified: minimal (25.7%); somatic (50.7%) and severe (23.6%). In the severe class for eight out nine depressive symptoms the probabilities were above 50%, and the probability of suicidal ideation was almost a third in this class. Being female (Adjusted Odds ratio [AOR], 2.49; 95% confidence interval [CI] [1.63-3.81]), current smoker (AOR, 1.74; 95% CI [1.15-2.65]), having basic education (AOR, 3.12; 95% CI [1.30-7.53]) and obesity (AOR, 2.72; 95% CI [1.61-4.59]) significantly increased the likelihood of belonging to severe class. Higher social support decreased the odds of being in the somatic (OR, 0.96; 95% CI [0.93-0.98]) and severe (OR, 0.92; 95% CI [0.90-0.94]) classes.

CONCLUSIONS

These findings highlight the importance of individualized intervention strategies for depression management. Also, the study suggests that nutritional status and social support should be considered when addressing depression in this population.

The status of MRI databases across the world focused on psychiatric and neurological disorders

Neuroimaging databases for neuro-psychiatric disorders enable researchers to implement data-driven research approaches by providing access to rich data that can be used to study disease, build and validate machine learning models, and even redefine disease spectra. The importance of sharing large, multi-center, multi-disorder databases has gradually been recognized in order to truly translate brain imaging knowledge into real-world clinical practice. Here, we review MRI databases that share data globally to serve multiple psychiatric or neurological disorders. We found 42 datasets consisting of 23,293 samples from patients with psychiatry and neurological disorders and healthy controls; 1245 samples from mood disorders (major depressive disorder and bipolar disorder), 2015 samples from developmental disorders (autism spectrum disorder, attention-deficit hyperactivity disorder), 675 samples from schizophrenia, 1194 samples from Parkinson's disease, 5865 samples from dementia (including Alzheimer's disease), We recognize that large, multi-center databases should include governance processes that allow data to be shared across national boundaries. Addressing technical and regulatory issues of existing databases can lead to better design and implementation and improve data access for the research community. The current trend toward the development of shareable MRI databases will contribute to a better understanding of the pathophysiology, diagnosis and assessment, and development of early interventions for neuropsychiatric disorders.

Subtyping drug-free first-episode major depressive disorder based on cortical surface area alterations

BACKGROUND

Major depressive disorder (MDD) is recognized as a complex and heterogeneous metal illness, characterized by diverse clinical symptoms and variable treatment outcomes. Previous studies have repeatedly reported alterations in brain morphology in MDD, but findings vary across sample characteristics. Whether this neurobiological substrate could stratify MDD into more homogeneous clinical subgroups thus improving personalized medicine remains unknown.

METHODS

We included 65 drug-free patients with first-episode MDD and 66 healthy controls (HCs) and collected their structural MRI data. We performed the surface reconstruction and calculated cortical surface area using Freesurfer. The surface area of 34 Gy matter regions in each hemisphere based on the Desikan-Killiany atlas were extracted for each participant and subtyping results were obtained with the Louvain community detection algorithm. The demographic and clinical characteristics were then compared between MDD subgroups.

RESULTS

Two subgroups defined by distinct patterns of cortical surface area were identified in first-episode MDD. Subgroup 1 exhibited a significant reduction in surface area across nearly the entire cortex compared to subgroup 2 and HCs, whereas subgroup 2 demonstrated increased surface area than HCs. Further, subgroup 1 exhibited a higher proportion of females, and higher severity of anxiety symptoms compared to subgroup 2.

LIMITATIONS

The relatively small sample size.

CONCLUSIONS

This study identified two neurobiologically subgroups with distinct alterations in cortical surface area among drug-free patients with first-episode MDD. Our results highlight the promise of in delineating morphological heterogeneity within MDD, particularly in relation to the severity of anxiety symptoms.

Genome-wide meta-analysis of ascertainment and symptom structures of major depression in case-enriched and community cohorts

BACKGROUND

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and etiological subtypes. There are several challenges to integrating symptom data from genetically informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data.

METHODS

We conducted genome-wide association studies of major depressive symptoms in three cohorts that were enriched for participants with a diagnosis of depression (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts who were not recruited on the basis of diagnosis (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors.

RESULTS

The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for the skip-structure in community cohorts (use of Depression and Anhedonia as gating symptoms).

CONCLUSION

The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analyzing genetic association data.

Multimodal brain-derived subtypes of Major depressive disorder differentiate patients for anergic symptoms, immune-inflammatory markers, history of childhood trauma and treatment-resistance

An estimated 30 % of Major Depressive Disorder (MDD) patients exhibit resistance to conventional antidepressant treatments. Identifying reliable biomarkers of treatment-resistant depression (TRD) represents a major goal of precision psychiatry, which is hampered by the clinical and biological heterogeneity. To uncover biologically-driven subtypes of MDD, we applied an unsupervised data-driven framework to stratify 102 MDD patients on their neuroimaging signature, including extracted measures of cortical thickness, grey matter volumes, and white matter fractional anisotropy. Our novel analytical pipeline integrated different machine learning algorithms to harmonize data, perform data dimensionality reduction, and provide a stability-based relative clustering validation. The obtained clusters were characterized for immune-inflammatory peripheral biomarkers, TRD, history of childhood trauma and depressive symptoms. Our results indicated two different clusters of patients, differentiable with 67 % of accuracy: one cluster (n = 59) was associated with a higher proportion of TRD, and higher scores of energy-related depressive symptoms, history of childhood abuse and emotional neglect; this cluster showed a widespread reduction in cortical thickness (d = 0.43-1.80) and volumes (d = 0.45-1.05), along with fractional anisotropy in the fronto-occipital fasciculus, stria terminalis, and corpus callosum (d = 0.46-0.52); the second cluster (n = 43) was associated with cognitive and affective depressive symptoms, thicker cortices and wider volumes. Multivariate analyses revealed distinct brain-inflammation relationships between the two clusters, with increase in pro-inflammatory markers being associated with decreased cortical thickness and volumes. Our stratification of MDD patients based on structural neuroimaging identified clinically-relevant subgroups of MDD with specific symptomatic and immune-inflammatory profiles, which can contribute to the development of tailored personalized interventions for MDD.

Latent Profiles of Childhood Adversity, Adolescent Mental Health, and Neural Network Connectivity

Importance

Adverse childhood experiences are pervasive and heterogeneous, with potential lifelong consequences for psychiatric morbidity and brain health. Existing research does not capture the complex interplay of multiple adversities, resulting in a lack of precision in understanding their associations with neural function and mental health.

Objectives

To identify distinct childhood adversity profiles and examine their associations with adolescent mental health and brain connectivity.

Design, Setting, and Participants

This population-based birth cohort used data for children who were born in 20 large US cities between 1998 and 2000 and participated in the Future Families and Child Well-Being Study. Families were interviewed when children were born and at ages 1, 3, 5, 9, and 15 years. At age 15 years, neuroimaging data were collected from a subset of these youths. Data were collected from February 1998 to April 2017. Analyses were conducted from March to December 2023.

Exposures

Latent profiles of childhood adversity, defined by family and neighborhood risks across ages 0 to 9 years.

Main Outcomes and Measures

Internalizing and externalizing symptoms at age 15 years using parent- and youth-reported measures. Profile-specific functional magnetic resonance imaging connectivity across the default mode network (DMN), salience network (SN), and frontoparietal network (FPN).

Results

Data from 4210 individuals (2211 [52.5%] male; 1959 [46.5%] Black, 1169 [27.7%] Hispanic, and 786 [18.7%] White) revealed 4 childhood adversity profiles: low-adversity (1230 individuals [29.2%]), medium-adversity (1973 [46.9%]), high-adversity (457 [10.9%]), and high maternal depression (MD; 550 [13.1%]). High-adversity, followed by MD, profiles had the highest symptoms. Notably, internalizing symptoms did not differ between these 2 profiles (mean difference, 0.11; 95% CI, -0.03 to 0.26), despite the MD profile showing adversity levels most similar to the medium-adversity profile. In the neuroimaging subsample of 167 individuals (91 [54.5%] female; 128 [76.6%] Black, 11 [6.6%] Hispanic, and 20 [12.0%] White; mean [SD] age, 15.9 [0.5] years), high-adversity and MD profiles had the highest DMN density relative to other profiles (F(3,163) = 11.14; P < .001). The high-adversity profile had lower SN density relative to the low-adversity profile (mean difference, -0.02; 95% CI, -0.04 to -0.003) and the highest FPN density among all profiles (F(3,163) = 18.96; P < .001). These differences were specific to brain connectivity during an emotion task, but not at rest.

Conclusions and Relevance

In this cohort study, children who experienced multiple adversities, or only elevated MD, had worse mental health and different neural connectivity in adolescence. Interventions targeting multiple risk factors, with a focus on maternal mental health, could produce the greatest benefits.

Overcoming treatment-resistant depression with machine-learning based tools: a study protocol combining EEG and clinical data to personalize glutamatergic and brain stimulation interventions (SelecTool Project)

Treatment-Resistant Depression (TRD) poses a substantial health and economic challenge, persisting as a major concern despite decades of extensive research into novel treatment modalities. The considerable heterogeneity in TRD's clinical manifestations and neurobiological bases has complicated efforts toward effective interventions. Recognizing the need for precise biomarkers to guide treatment choices in TRD, herein we introduce the SelecTool Project. This initiative focuses on developing (WorkPlane 1/WP1) and conducting preliminary validation (WorkPlane 2/WP2) of a computational tool (SelecTool) that integrates clinical data, neurophysiological (EEG) and peripheral (blood sample) biomarkers through a machine-learning framework designed to optimize TRD treatment protocols. The SelecTool project aims to enhance clinical decision-making by enabling the selection of personalized interventions. It leverages multi-modal data analysis to navigate treatment choices towards two validated therapeutic options for TRD: esketamine nasal spray (ESK-NS) and accelerated repetitive Transcranial Magnetic Stimulation (arTMS). In WP1, 100 subjects with TRD will be randomized to receive either ESK-NS or arTMS, with comprehensive evaluations encompassing neurophysiological (EEG), clinical (psychometric scales), and peripheral (blood samples) assessments both at baseline (T0) and one month post-treatment initiation (T1). WP2 will utilize the data collected in WP1 to train the SelecTool algorithm, followed by its application in a second, out-of-sample cohort of 20 TRD subjects, assigning treatments based on the tool's recommendations. Ultimately, this research seeks to revolutionize the treatment of TRD by employing advanced machine learning strategies and thorough data analysis, aimed at unraveling the complex neurobiological landscape of depression. This effort is expected to provide pivotal insights that will promote the development of more effective and individually tailored treatment strategies, thus addressing a significant void in current TRD management and potentially reducing its profound societal and economic burdens.

Characterization of depression subtypes and their relationships to stressor profiles among middle-aged and older adults: An analysis of the canadian longitudinal study on aging (CLSA)

The current diagnostic criteria for depression do not sufficiently reflect its heterogeneous clinical presentations. Associations between adverse childhood experiences (ACEs), allostatic load (AL), and depression subtypes have not been extensively studied. Depression subtypes were determined based on clinical presentations, and their relationships to AL biomarkers and ACEs were elucidated in a sample of middle-aged and older adults. Participants from the Canadian Longitudinal Study on Aging who screened positive for depression were included (n = 3966). Depression subtypes, AL profiles and ACE profiles were determined with latent profile analyses, and associations between them were determined using multinomial logistic regression. Four depression subtypes were identified: positive affect, melancholic, typical, and atypical. Distinct associations between depression subtypes, stressor profiles and covariates were observed. Among the subtypes compared to positive affect, atypical subtype had the most numerous significant associations, and the subtypes had unique relationships to stressor profiles. Age, sex, smoking status, chronic conditions, marital status, and physical activity were significant covariates. The present study describes distinct associations between depression subtypes and measures of stress (objective and self-reported), as well as related factors that differentiate subtypes. The findings may inform more targeted and integrated clinical management strategies for depression in individuals exposed to multiple stressors.

Aberrant associations between neuronal resting-state fluctuations and working memory-induced activity in major depressive disorder

Previous investigations have revealed performance deficits and altered neural processes during working-memory (WM) tasks in major depressive disorder (MDD). While most of these studies used task-based functional magnetic resonance imaging (fMRI), there is an increasing interest in resting-state fMRI to characterize aberrant network dynamics involved in this and other MDD-associated symptoms. It has been proposed that activity during the resting-state represents characteristics of brain-wide functional organization, which could be highly relevant for the efficient execution of cognitive tasks. However, the dynamics linking resting-state properties and task-evoked activity remain poorly understood. Therefore, the present study investigated the association between spontaneous activity as indicated by the amplitude of low frequency fluctuations (ALFF) at rest and activity during an emotional n-back task. 60 patients diagnosed with an acute MDD episode, and 52 healthy controls underwent the fMRI scanning procedure. Within both groups, positive correlations between spontaneous activity at rest and task-activation were found in core regions of the central-executive network (CEN), whereas spontaneous activity correlated negatively with task-deactivation in regions of the default mode network (DMN). Compared to healthy controls, patients showed a decreased rest-task correlation in the left prefrontal cortex (CEN) and an increased negative correlation in the precuneus/posterior cingulate cortex (DMN). Interestingly, no significant group-differences within those regions were found solely at rest or during the task. The results underpin the potential value and importance of resting-state markers for the understanding of dysfunctional network dynamics and neural substrates of cognitive processing.

Depression Severity, Slow- versus Fast-Wave Neural Activity, and Symptoms of Melancholia

Melancholia is a major and severe subtype of depression, with only limited data regarding its association with neurological phenomena. To extend the current understanding of how particular aspects of melancholia are correlated with brain activity, electroencephalographic data were collected from 100 adults (44 males and 56 females, all aged 18 y or more) and investigated for the association between symptoms of melancholia and the ratios of alpha/beta activity and theta/beta activity at parietal-occipital EEG sites PO1 and PO2. The results indicate differences in these associations according to the depressive status of participants and the particular symptom of melancholia. Depressed participants exhibited meaningfully direct correlations between alpha/beta and theta/beta activity and the feeling that "Others would be better off if I was dead" at PO1, whereas non-depressed participants had significant inverse correlations between theta/beta activity and "Feeling useless and not needed" and "I find it hard to make decisions" at PO1. The results are discussed in terms of the relative levels of fast-wave (beta) versus slow-wave (alpha, theta) activity exhibited by depressed and non-depressed participants in the parietal-occipital region and the cognitive activities that are relevant to that region.

Multi-polygenic prediction of frailty highlights chronic pain and educational attainment as key risk and protective factors

Frailty is a complex trait. Twin studies and recent Genome-Wide Association Studies have demonstrated a strong genetic basis of frailty but there remains a lack of genetic studies exploring genetic prediction of Frailty. Previous work has shown that a single polygenic predictor - represented by a Frailty polygenic score - predicts Frailty, measured via the frailty index, in independent samples within the United Kingdom. We extended this work, using a multi-polygenic score (MPS) approach to increase predictive power. Predictor variables - twenty-six polygenic scores (PGS) were modelled in regularised Elastic net regression models, with repeated cross-validation, to estimate joint prediction of the polygenic scores and order the predictions by their contributing strength to Frailty in two independent cohorts aged 65+ - the English Longitudinal Study of Ageing (ELSA) and Lothian Birth Cohort 1936 (LBC1936). Results showed that the MPS explained 3.6% and 4.7% of variance compared to the best single-score prediction of 2.6% and 2.2% of variance in ELSA and LBC1936 respectively. The strongest polygenic predictors of worsening frailty came from PGS for Chronic pain, Frailty and Waist circumference; whilst PGS for Parental Death, Educational attainment, and Rheumatoid Arthritis were found to be protective to frailty. Results from the predictors remaining in the final model were then validated using the longitudinal LBC1936, with equivalent PGS scores from the same GWAS summary statistics. Thus, this MPS approach provides new evidence for the genetic contributions to frailty in later life and sheds light on the complex structure of the Frailty Index measurement.

Gray matter atrophy is constrained by normal structural brain network architecture in depression

BACKGROUND

There is growing evidence that gray matter atrophy is constrained by normal brain network (or connectome) architecture in neuropsychiatric disorders. However, whether this finding holds true in individuals with depression remains unknown. In this study, we aimed to investigate the association between gray matter atrophy and normal connectome architecture at individual level in depression.

METHODS

In this study, 297 patients with depression and 256 healthy controls (HCs) from two independent Chinese dataset were included: a discovery dataset (105 never-treated first-episode patients and matched 130 HCs) and a replication dataset (106 patients and matched 126 HCs). For each patient, individualized regional atrophy was assessed using normative model and brain regions whose structural connectome profiles in HCs most resembled the atrophy patterns were identified as putative epicenters using a backfoward stepwise regression analysis.

RESULTS

In general, the structural connectome architecture of the identified disease epicenters significantly explained 44% (±16%) variance of gray matter atrophy. While patients with depression demonstrated tremendous interindividual variations in the number and distribution of disease epicenters, several disease epicenters with higher participation coefficient than randomly selected regions, including the hippocampus, thalamus, and medial frontal gyrus were significantly shared by depression. Other brain regions with strong structural connections to the disease epicenters exhibited greater vulnerability. In addition, the association between connectome and gray matter atrophy uncovered two distinct subgroups with different ages of onset.

CONCLUSIONS

These results suggest that gray matter atrophy is constrained by structural brain connectome and elucidate the possible pathological progression in depression.

Sustained antidepressant effects of ketamine metabolite involve GABAergic inhibition-mediated molecular dynamics in aPVT glutamatergic neurons

Despite the rapid and sustained antidepressant effects of ketamine and its metabolites, their underlying cellular and molecular mechanisms are not fully understood. Here, we demonstrate that the sustained antidepressant-like behavioral effects of (2S,6S)-hydroxynorketamine (HNK) in repeatedly stressed animal models involve neurobiological changes in the anterior paraventricular nucleus of the thalamus (aPVT). Mechanistically, (2S,6S)-HNK induces mRNA expression of extrasynaptic GABAA receptors and subsequently enhances GABAA-receptor-mediated tonic currents, leading to the nuclear export of histone demethylase KDM6 and its replacement by histone methyltransferase EZH2. This process increases H3K27me3 levels, which in turn suppresses the transcription of genes associated with G-protein-coupled receptor signaling. Thus, our findings shed light on the comprehensive cellular and molecular mechanisms in aPVT underlying the sustained antidepressant behavioral effects of ketamine metabolites. This study may support the development of potentially effective next-generation pharmacotherapies to promote sustained remission of stress-related psychiatric disorders.

Individual deviations from normative electroencephalographic connectivity predict antidepressant response

BACKGROUND

Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders.

METHODS

We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment.

RESULTS

We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses.

CONCLUSIONS

Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment.

TRIAL REGISTRATION

Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.

Olfactory genes affect major depression in highly educated, emotionally stable, lean women: a bridge between animal models and precision medicine

Most current approaches to establish subgroups of depressed patients for precision medicine aim to rely on biomarkers that require highly specialized assessment. Our present aim was to stratify participants of the UK Biobank cohort based on three readily measurable common independent risk factors, and to investigate depression genomics in each group to discover common and separate biological etiology. Two-step cluster analysis was run separately in males (n = 149,879) and females (n = 174,572), with neuroticism (a tendency to experience negative emotions), body fat percentage, and years spent in education as input variables. Genome-wide association analyses were implemented within each of the resulting clusters, for the lifetime occurrence of either a depressive episode or recurrent depressive disorder as the outcome. Variant-based, gene-based, gene set-based, and tissue-specific gene expression test were applied. Phenotypically distinct clusters with high genetic intercorrelations in depression genomics were found. A two-cluster solution was the best model in each sex with some differences including the less important role of neuroticism in males. In females, in case of a protective pattern of low neuroticism, low body fat percentage, and high level of education, depression was associated with pathways related to olfactory function. While also in females but in a risk pattern of high neuroticism, high body fat percentage, and less years spent in education, depression showed association with complement system genes. Our results, on one hand, indicate that alteration of olfactory pathways, that can be paralleled to the well-known rodent depression models of olfactory bulbectomy, might be a novel target towards precision psychiatry in females with less other risk factors for depression. On the other hand, our results in multi-risk females may provide a special case of immunometabolic depression.

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response

Introduction

To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response.

Methods

Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients.

Results

Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization.

Discussion

Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability.

Clinical trial registration

https://clinicaltrials.gov, identifier NCT00519428.

Lipid parameters and depression in patients with chronic tinnitus: A cross-sectional observation

BACKGROUND

Pathophysiological theories assume importance of metabolic abnormalities in patients with major depression - and possibly chronic tinnitus. Although chronic tinnitus frequently correlates with depression, links between high-density lipoprotein (HDL) and depression are uninvestigated.

METHODS

Two-hundred patients with chronic tinnitus (Mage = 55; 51% female) were examined. Serum levels of total cholesterol (TC), triglycerides (TGs), HDL, low-density lipoprotein cholesterol (LDL), non-HDL, as well as LDL/HDL and TC/HDL ratios were analysed. Questionnaires included depression subscales of the ICD-10 Symptom Rating, the Hospital Anxiety and Depression Scale (HADS_D), and the Berlin Mood Questionnaire (BSF). Multivariate analyses of covariance and linear regression models - which controlled age, tinnitus-related distress and perceived stress - investigated between-subgroup differences (p < 0.05) and linear associations between HDL indices and depression (p < 0.01).

RESULTS

HDL levels did not differ for tinnitus-symptom durations, smoking and alcohol use levels, statin or antihypertensive drug use, and body-mass indices. Relative to non-to-mildly depressed patients with chronic tinnitus, patients with moderate-to-severe depression (n = 45; 23%) had significantly lower HDL levels (d = -0.35) and higher LDL/HDL (d = 0.39) and TC/HDL ratios (d = 0.40). Across participants, HDL-levels were negatively associated with depression as measured by the HADS_D and BSF_indifference scales.

CONCLUSIONS

In keeping with general depression research, low serum HDL levels correlate with depressive symptomatology in patients with chronic tinnitus. This association may be influenced by proximal (e.g. modulations of HPA-axis activity) or distal factors (e.g. maladaptive coping behaviours) - both of which should be conceptualized within psychological stimulus-processing frameworks.

Discrete prefrontal neuronal circuits determine repeated stress-induced behavioral phenotypes in male mice

Chronic stress is a major risk factor for psychiatric disorders, including depression. Although depression is a highly heterogeneous syndrome, it remains unclear how chronic stress drives individual differences in behavioral responses. In this study, we developed a subtyping-based approach wherein stressed male mice were divided into four subtypes based on their behavioral patterns of social interaction deficits and anhedonia, the core symptoms of psychiatric disorders. We identified three prefrontal cortical neuronal projections that regulate repeated stress-induced behavioral phenotypes. Among them, the medial prefrontal cortex (mPFC)→anterior paraventricular thalamus (aPVT) pathway determines the specific behavioral subtype that exhibits both social deficits and anhedonia. Additionally, we identified the circuit-level molecular mechanism underlying this subtype: KDM5C-mediated epigenetic repression of Shisa2 transcription in aPVT projectors in the mPFC led to social deficits and anhedonia. Thus, we provide a set of biological aspects at the cellular, molecular, and epigenetic levels that determine distinctive stress-induced behavioral phenotypes.

Data-driven subgrouping of youths with depression reveals that resilience is associated with higher physical functioning despite high symptom burden in the Texas Youth Depression and Suicide Research Network (TX-YDSRN)

BACKGROUND

The Patient-Reported Outcomes Measurement Information System (PROMIS) measure, which assesses past week status of seven domains (physical function mobility, anxiety, depressive symptoms, fatigue, peer relationships, pain interference, and pain intensity), represents a new paradigm using patient-reported outcomes. We used a data-driven approach with PROMIS to identify subgroups of youths receiving depression treatment.

METHODS

Youths (n = 721) enrolled in the Texas Youth Depression and Suicide Research Network who completed the PROMIS were analyzed. Latent class analyses (LCAs) identified subgroups and compared their baseline clinical/sociodemographic features.

RESULTS

Compared to population norms, our sample had worse than average physical function, anxiety, depression, fatigue, and pain interference. Using LCA, four subgroups were identified: 1) lower symptom severity and higher physical functioning (14.6 %); 2) higher symptom burden, higher pain interference/intensity, and lower physical functioning (52.7 %); 3) higher symptom burden, higher pain interference/intensity, but with higher physical functioning (9.2 %); and 4) higher symptom burden, but lower physical functioning and pain interference/intensity (23.6 %). Group 3 demonstrated higher resilience than Group 2. In contrast, Group 2 had higher anxiety than Group 4.

LIMITATIONS

Individuals may have different symptom profiles due to the observational nature of the study. Replication of these subgroups may be difficult, as future samples may differ in these characteristics. Further work may demonstrate the stability of these groups.

CONCLUSIONS

A data-driven analysis identified a small but significant subgroup with high physical functioning despite high symptom burden and pain, and this group reported higher resilience. Resilience-enhancing interventions may help improve functional outcomes in depressed youth.

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment

BACKGROUND

The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume.

METHODS

We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors.

RESULTS

In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination.

CONCLUSIONS

Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

A Delphi consensus on clinical features, diagnosis and treatment of major depressive disorder patients with anhedonia amongst psychiatrists in the Asia-Pacific

Background

Anhedonia, a core diagnostic feature for major depressive disorder (MDD), is defined as the loss of pleasure and interest in daily activities. Its prevalence in MDD patients vary from 35 to 70%. Anhedonia in MDD negatively impacts functioning and is associated with treatment resistance and poorer prognosis for various clinical outcomes. Owing to its complexity, there remains considerable heterogeneity in the conceptualization, diagnosis and clinical management of anhedonia in MDD.

Methods

This modified Delphi panel was conducted to elicit expert opinion and establish consensus on concepts relating to clinical features, diagnosis and treatment of MDD with anhedonia (MDDwA) amongst psychiatrists in the Asia-Pacific region. Seven themes were covered. A three-stage process was adopted for consensus generation (two online survey rounds, followed by a moderated consensus meeting). Statements were developed based on a literature review and input from a steering committee of six regional experts. The panel included 12 psychiatrists practicing in Australia, China, Hong Kong, Japan, South Korea and Taiwan with ≥5 years of specialist clinical experience, including assessment or management of patients with MDDwA.

Results

Overall, consensus was achieved (median ≥8) on 89/103 statements (86%). About half of the statements (55/103, 53%) achieved consensus in Round 1, and 29/36 modified statements achieved consensus in Round 2. At the moderated consensus meeting, five modified statements were discussed by the steering committee and consensus was achieved on all statements (5/5). The findings highlighted a lack of clear and practical methods in clinical practice for assessing anhedonia in MDD patients and limited physician awareness of anhedonia in Asia-Pacific.

Conclusion

Insights from this Delphi consensus provide a reference point for psychiatrists in Asia-Pacific to optimize their strategies for personalized diagnosis and management of patients with MDDwA. Identification of distinct and clinically relevant subtypes in MDD may be valuable for guiding personalized diagnosis and management approaches, including type-specific therapies.

The novel subtype of major depressive disorder characterized by somatic symptoms is associated with poor treatment efficacy and prognosis: A data-driven cluster analysis of a prospective cohort in China

BACKGROUND

There is not yet a valid and evidence-based system to classify patients with MDD into more homogeneous subtypes based on their clinical features. This study aims to identify symptom-based subtypes of MDD and investigate whether the treatment outcomes of those subtypes would be different.

METHOD

The cohort was established at 12 densely populated cities of China. A total of 1487 patients were enrolled. All participants were 18-65 years old and diagnosed with MDD. Participants were followed up at baseline, weeks 4, 8, and 12, and months 4 and 6. K-means algorithm was used to cluster patients with MDD according to clinical symptoms. The network analysis was adopted to characterize and compare the symptom patterns in the clusters. We also examined the associations between the clusters and the clinical outcomes.

RESULTS

The optimal number of the clusters was determined to be 2. Each cluster's maximum Jaccard Co-efficient was calculated to be >0.5 (cluster1 = 0.53, cluster 2 = 0.67). The symptom "depressed mood" and some other affective symptoms were the most prominent in cluster 1. Somatic symptoms, such as weight loss and general somatic symptoms, had the greatest expected influence in cluster 2. Compared with the response rates of the patients in the "somatic cluster", those of the patients in the "affective cluster" were significantly higher (P < 0.05).

CONCLUSIONS

Patients with MDD might be classified into two symptom-based subtypes featured with affective symptoms or somatic symptoms. The treatment efficacy and prognosis of the subtype featured with somatic symptoms may be worse.

Association of 20-Year Longitudinal Depressive Symptoms With Left Ventricular Geometry Outcomes in the Coronary Artery Risk Development in Young Adults Study: A Role for Androgens?

OBJECTIVE

Depression is a risk factor for coronary heart disease and left ventricular hypertrophy (LVH) is a potent predictor of coronary heart disease events. Whether depression is associated with LVH has received limited investigation. This study assessed cross-sectional and 20-year longitudinal associations of depressive symptoms with LVH outcomes after accounting for important known confounders.

METHODS

From 5115 participants enrolled in 1985-1986 in the Coronary Artery Risk Development in Young Adults Study, 2533 had serial measures of depressive symptoms and subsequent echocardiography to measure normal LV geometry, concentric remodeling, and LVH. The primary exposure variable was trajectories of the Center for Epidemiologic Studies Depression (CES-D) scale score from 1990-1991 to 2010-2011. Multivariable polytomous logistic regression was used to assess associations of trajectories with a composite LV geometry outcome created using echocardiogram data measured in 2010-2011 and 2015-2016. Sex-specific conflicting results led to exploratory models that examined potential importance of testosterone and sex hormone-binding globulin.

RESULTS

Overall CES-D and Somatic subscale trajectories had significant associations with LVH for female participants only. Odds ratios for the subthreshold (mean CES-D ≈ 14) and stable (mean CES-D ≈ 19) groups were 1.49 (95% confidence interval = 1.05-2.13) and 1.88 (95% confidence interval = 1.16-3.04), respectively. For female participants, sex hormone-binding globulin was inversely associated with LVH, and for male participants, bioavailable testosterone was positively associated with concentric geometry.

CONCLUSIONS

Findings from cross-sectional and longitudinal regression models for female participants, but not male ones, and particularly for Somatic subscale trajectories suggested a plausible link among depression, androgens, and LVH. The role of androgens to the depression-LVH relation requires additional investigation in future studies.

Increased brain nucleus accumbens functional connectivity in melancholic depression

BACKGROUND

Melancholic depression, marked by typical symptoms of anhedonia, is regarded as a homogeneous subtype of major depressive disorder (MDD). However, little attention was paid to underlying mechanisms of melancholic depression. This study aims to examine functional connectivity of the reward circuit associated with anhedonia symptoms in melancholic depression.

METHODS

Fifty-nine patients with first-episode drug- naive MDD, including 31 melancholic patients and 28 non-melancholic patients, were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two healthy volunteers were recruited as controls. Bilateral nucleus accumbens (NAc) were selected as seed points to form functional NAc network. Then support vector machine (SVM) was used to distinguish melancholic patients from non-melancholic patients.

RESULTS

Relative to non-melancholic patients, melancholic patients displayed increased functional connectivity (FC) between bilateral NAc and right middle frontal gyrus (MFG) and between right NAc and left cerebellum lobule VIII. Compared to healthy controls, melancholic patients showed increased FC between right NAc and right lingual gyrus and between left NAc and left postcentral gyrus; non-melancholic patients had increased FC between bilateral NAc and right lingual gyrus. No significant correlations were observed between altered FC and clinical variables in melancholic patients. SVM results showed that FC between left NAc and right MFG could accurately distinguish melancholic patients from non-melancholic patients.

CONCLUSION

Melancholic depression exhibited different patterns of functional connectivity of the reward circuit relative to non-melancholic patients. This study highlights the significance of the reward circuit in the neuropathology of melancholic depression.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Adenylate cyclase 3: a potential genetic link between obesity and major depressive disorder

Obesity and major depressive disorder (MDD) are both significant health issues that have been increasing in prevalence and are associated with multiple comorbidities. Obesity and MDD have been shown to be bidirectionally associated, and they are both influenced by genetics and environmental factors. However, the molecular mechanisms that link these two diseases are not yet fully understood. It is possible that these diseases are connected through the actions of the cAMP/protein kinase A (PKA) pathway. Within this pathway, adenylate cyclase 3 (Adcy3) has emerged as a key player in both obesity and MDD. Numerous genetic variants in Adcy3 have been identified in humans in association with obesity. Rodent knockout studies have also validated the importance of this gene for energy homeostasis. Furthermore, Adcy3 has been identified as a top candidate gene and even a potential blood biomarker for MDD. Adcy3 and the cAMP/PKA pathway may therefore serve as an important genetic and functional link between these two diseases. In this mini-review, we discuss the role of both Adcy3 and the cAMP/PKA pathway, including specific genetic mutations, in both diseases. Understanding the role that Adcy3 mutations play in obesity and MDD could open the door for precision medicine approaches and treatments for both diseases that target this gene.

Assessment of biomarker stability and assay performance parameters for medical diagnosis: a case study of diagnosis of major depressive disorder

Aim: Assessing the stability profiles and assay performance of 24 biomarker assays in 32 biomarker/body fluid combinations identified as relevant for prediction of major depressive disorder. Materials & methods: Combinations were tested for stability and assay performance with ELISA at different storage and freeze-thaw conditions in pooled samples of 40 patients. Results: Stability and assay performance issues were found in almost all cases except three biomarkers in urine and three in serum. Conclusion: This study shows that, to produce reliable measurement data, assessments of stability and assay performance are essential. In development, other quality assurance parameters might be implemented to increase the level of measurement reliability by increasing assay performance control.

Trying to name what doesn't change: Neural nonresponse to Cognitive Therapy for depression

BACKGROUND

Theoretical models of neural mechanisms underlying Cognitive Behavior Therapy (CBT) for major depressive disorder (MDD) propose that psychotherapy changes neural functioning of prefrontal cortical structures associated with cognitive-control processes (DeRubeis, Siegle, & Hollon, ); however, MDD is persistent and characterized by long-lasting vulnerabilities to recurrence after intervention, suggesting that underlying neural mechanisms of MDD remain despite treatment. It follows that identification of treatment-resistant aberrant neural processes in MDD may inform clinical and research efforts targeting sustained remission. Thus, we sought to identify brain regions showing aberrant neural functioning in MDD that either (1) fail to exhibit substantive change (nonresponse) or (2) exhibit functional changes (response) following CBT.

METHODS

To identify treatment-resistant neural processes (as well as neural processes exhibiting change after treatment), we collected functional magnetic resonance imaging (fMRI) data of MDD patients (n = 58) before and after CBT as well as never-depressed controls (n = 35) before and after a similar amount of time. We evaluated fMRI data using conjunction analyses, which utilized several contrast-based criteria to characterize brain regions showing both differences between patients and controls at baseline and nonresponse or response to CBT.

RESULTS

Findings revealed nonresponse in a cerebellar region and response in prefrontal and parietal regions.

CONCLUSIONS

Results are consistent with prior theoretical models of CBT's direct effect on cortical regulatory processes but expand on them with identification of additional regions (and associated neural systems) of response and nonresponse to CBT.

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

BACKGROUND

Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking.

METHODS

Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity.

RESULTS

Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes.

CONCLUSIONS

These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.

Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study

Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.

Effect of modernized collaborative care for depression on brain-derived neurotrophic factor (BDNF) and depressive symptom clusters: Data from the eIMPACT trial

Brain-derived neurotrophic factor (BDNF) levels are lower in people with depression and are normalized following pharmacological treatment. However, it is unknown if psychological treatments for depression improve BDNF and if change in BDNF is a mediator of intervention effects on depressive symptoms. Therefore, using data from the eIMPACT trial, we sought to determine the effect of modernized collaborative care for depression on 12-month changes in BDNF and cognitive/affective and somatic depressive symptom clusters and to examine whether BDNF changes mediate intervention effects on depressive symptoms. 216 primary care patients with depression from a safety net healthcare system were randomized to 12 months of the eIMPACT intervention (internet cognitive-behavioral therapy [CBT], telephonic CBT, and select antidepressant medications) or usual primary care. Plasma BDNF was measured with commercially available kits, and depressive symptom clusters were assessed by the Patient Health Questionnaire-9. The intervention did not influence BDNF but did improve both the cognitive/affective and somatic clusters over 12 months. Changes in BDNF did not mediate the intervention effect on either cluster. Our findings suggest that modernized collaborative care is an effective treatment for both the cognitive/affective and somatic symptoms of depression and that the mechanism of action is not improvements in BDNF. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02458690.

Quantification of Dysautonomia in Major Depressive Disorder Using the Composite Autonomic Scoring Scale-31 (COMPASS-31)

Background Dysautonomia denotes an alteration in the autonomic nervous system that can significantly lead to multiorgan failure. Conversely, depression not only affects cognitive functions but also poses a risk factor for sudden cardiac death. In our study, the Composite Autonomic Scoring-31 (COMPASS-31) is used to quantify the autonomic symptoms present if any in patients with depression. Materials and methods Forty-two patients with major depressive disorder (MDD) were recruited using a PHQ-9 questionnaire followed by a COMPASS-31 scale to quantify dysautonomia symptoms, and they were compared with healthy controls. Further regression analysis was conducted to establish any relationship between independent variables and COMPASS-31 scores. Results  The average COMPASS-31 score in patients with MDD was 22.56±8.42, which was significantly increased compared to healthy controls (p=0.001). Furthermore, the differences persisted across various subdomains of the COMPASS-31 scale relative to severity of depression. Conclusion The study observations could provide a relevant perception regarding the association between depression and autonomic dysfunction with the use of a simple and brief yet validated instrument COMPASS-31, which can be utilized for screening at the primary care level.

Valence in the eyes: An emotion decoding profile of adults with major depressive disorder and a history of childhood maltreatment

BACKGROUND

Individuals with major depressive disorder (MDD) and childhood maltreatment have been proposed to constitute a subgroup with worse illness course and outcomes. To elucidate a potential social cognitive vulnerability in this subgroup, this study compared the emotion decoding abilities of MDD patients with and without a history of childhood maltreatment.

METHODS

Participants with a diagnosis of MDD were recruited from nationwide mental health organizations. Emotion decoding abilities were assessed using the Reading the Mind in the Eyes Test, while childhood maltreatment was measured with the Adverse Childhood Experiences Questionnaire.

RESULTS

The MDD patients with a history of childhood maltreatment exhibited poorer emotion decoding abilities than MDD patients without such past. This difference applied specifically to the decoding of positive and negative emotions, while no group differences emerged for the decoding of neutral emotions. When specific maltreatment types were considered as predictors only emotional neglect was associated with lower emotion decoding abilities. These associations remained when adjusting for demographic and clinical covariates.

CONCLUSIONS

By indicating that emotion decoding difficulties characterize the MDD subgroup with childhood maltreatment, the findings highlight a potential vulnerability that merits further examination in terms of its developmental antecedents and prognostic relevance.

Resolving heterogeneity in depression using individualized structural covariance network analysis

BACKGROUND

Elucidating individual aberrance is a critical first step toward precision medicine for heterogeneous disorders such as depression. The neuropathology of depression is related to abnormal inter-regional structural covariance indicating a brain maturational disruption. However, most studies focus on group-level structural covariance aberrance and ignore the interindividual heterogeneity. For that reason, we aimed to identify individualized structural covariance aberrance with the help of individualized differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted anatomical images of 195 first-episode untreated patients with depression and matched healthy controls (n = 78) were acquired. We obtained IDSCN for each patient and identified subtypes of depression based on shared differential edges.

RESULTS

As a result, patients with depression demonstrated tremendous heterogeneity in the distribution of differential structural covariance edges. Despite this heterogeneity, altered edges within subcortical-cerebellum network were often shared by most of the patients. Two robust neuroanatomical subtypes were identified. Specifically, patients in subtype 1 often shared decreased motor network-related edges. Patients in subtype 2 often shared decreased subcortical-cerebellum network-related edges. Functional annotation further revealed that differential edges in subtype 2 were mainly implicated in reward/motivation-related functional terms.

CONCLUSIONS

In conclusion, we investigated individualized differential structural covariance and identified that decreased edges within subcortical-cerebellum network are often shared by patients with depression. The identified two subtypes provide new insights into taxonomy and facilitate potential clues to precision diagnosis and treatment of depression.

Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study

Depressive mood states in healthy populations are prevalent but often under-reported. Biases exist in self-reporting of depression in otherwise healthy individuals. Gait and balance control can serve as objective markers for identifying those individuals, particularly in real-world settings. We utilized inertial measurement units (IMU) to measure gait and balance control. An exploratory, cross-sectional design was used to compare individuals who reported feeling depressed at the moment (n = 49) with those who did not (n = 84). The Quality Assessment Tool for Observational Cohort and Cross-sectional Studies was employed to ensure internal validity. We recruited 133 participants aged between 18-36 years from the university community. Various instruments were used to evaluate participants' present depressive symptoms, sleep, gait, and balance. Gait and balance variables were used to detect depression, and participants were categorized into three groups: not depressed, mild depression, and moderate-high depression. Participant characteristics were analyzed using ANOVA and Kruskal-Wallis tests, and no significant differences were found in age, height, weight, BMI, and prior night's sleep between the three groups. Classification models were utilized for depression detection. The most accurate model incorporated both gait and balance variables, yielding an accuracy rate of 84.91% for identifying individuals with moderate-high depression compared to non-depressed individuals.

Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

The high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Genetic structure of major depression symptoms across clinical and community cohorts

Diagnostic criteria for major depressive disorder allow for heterogeneous symptom profiles but genetic analysis of major depressive symptoms has the potential to identify clinical and aetiological subtypes. There are several challenges to integrating symptom data from genetically-informative cohorts, such as sample size differences between clinical and community cohorts and various patterns of missing data. We conducted genome-wide association studies of major depressive symptoms in three clinical cohorts that were enriched for affected participants (Psychiatric Genomics Consortium, Australian Genetics of Depression Study, Generation Scotland) and three community cohorts (Avon Longitudinal Study of Parents and Children, Estonian Biobank, and UK Biobank). We fit a series of confirmatory factor models with factors that accounted for how symptom data was sampled and then compared alternative models with different symptom factors. The best fitting model had a distinct factor for Appetite/Weight symptoms and an additional measurement factor that accounted for missing data patterns in the community cohorts (use of Depression and Anhedonia as gating symptoms). The results show the importance of assessing the directionality of symptoms (such as hypersomnia versus insomnia) and of accounting for study and measurement design when meta-analysing genetic association data.

Network analysis of frontal lobe alpha asymmetry confirms the neurophysiological basis of four subtypes of depressive behavior

Introduction

Although depression is widespread carries a major disease burden, current treatments remain non-universally effective, arguably due to the heterogeneity of depression, and leading to the consideration of depressive "subtypes" or "depressive behavior subtypes." One such model of depressive behavior (DB) subtypes was investigated for its associations with frontal lobe asymmetry (FLA), using a different data analytic procedure than in previous research in this field.

Methods

100 community volunteers (54 males, 46 females) aged between 18 yr. and 75 years (M = 32.53 yr., SD = 14.13 yr) completed the Zung Self-rating Depression Scale (SDS) and underwent 15 min of eyes closed EEG resting data collection across 10 frontal lobe sites. DB subtypes were defined on the basis of previous research using the SDS, and alpha-wave (8-13 Hz) data produced an index of FLA. Data were examined via network analysis.

Results

Several network analyses were conducted, producing two models of the association between DB subtypes and FLA, confirming unique neurophysiological profiles for each of the four DB subtypes.

Discussion

As well as providing a firm basis for using these DB subtypes in clinical settings, these findings provide a reasonable explanation for the inconsistency in previous FLA-depression research.

Biomarkers of central and peripheral inflammation mediate the association between HIV and depressive symptoms

People living with HIV are at increased risk for depression, though the underlying mechanisms for this are unclear. In the general population, depression is associated with peripheral and central inflammation. Given this, and since HIV infection elicits inflammation, we hypothesised that peripheral and central inflammatory biomarkers would at least partly mediate the association between HIV and depressive symptoms. People living with HIV (n = 125) and without HIV (n = 79) from the COmorBidity in Relation to AIDS (COBRA) cohort were included in this study. Participants living with and without HIV had similar baseline characteristics. All participants living with HIV were on antiretroviral therapy and were virally suppressed. Plasma, CSF, and brain MR spectroscopy (MRS) biomarkers were measured. Using logistic regression models adjusted for sociodemographic factors, we found that participants with HIV were more likely to have Any Depressive Symptoms (Patient Health Questionnaire [PHQ-9] score >4) (odds ratio [95% confidence interval] 3.27 [1.46, 8.09]). We then sequentially adjusted the models for each biomarker separately to determine the mediating role of each biomarker, with a >10% reduction in OR considered as evidence of potential mediation. Of the biomarkers analysed, MIG (-15.0%) and TNF-α (-11.4%) in plasma and MIP1-α (-21.0%) and IL-6 (-18.0%) in CSF mediated the association between HIV and depressive symptoms in this sample. None of the other soluble or neuroimaging biomarkers substantially mediated this association. Our findings suggest that certain biomarkers of central and peripheral inflammation may at least partly mediate the relationship between HIV and depressive symptoms.

The genetic basis of major depressive disorder

The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions

Importance

Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions.

Objective

To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression.

Design, Setting, and Participants

This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023.

Interventions

Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management).

Main Outcomes and Measures

The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis.

Results

The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory.

Conclusions and Relevance

In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

Individual Deviations from Normative Electroencephalographic Connectivity Predict Antidepressant Response

Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo. This modest efficacy is partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment - the approved antidepressants only benefit a portion of patients, calling for personalized psychiatry based on individual-level prediction of treatment responses. Normative modeling, a framework that quantifies individual deviations in psychopathological dimensions, offers a promising avenue for the personalized treatment for psychiatric disorders. In this study, we built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients. We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between treatment responses. Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective MDD treatment.

Subtypes of insomnia revealed by the heterogeneity of neuroanatomical patterns: a structural MRI study

The current conflicting neuroimaging findings of insomnia disorder (ID) may be attributed to heterogeneity in ID. The present study aims to clarify the high heterogeneity in ID and examine the objective neurobiological subtypes of ID by using a novel machine learning method based on gray matter volumes (GMVs). We recruited 56 patients with ID and 73 healthy controls (HCs). The T1-weighted anatomical images were obtained for each participant. We investigated whether the ID has higher interindividual heterogeneity in GMVs. Then, we used a heterogeneous machine learning algorithm by discriminative analysis (HYDRA) to identify subtypes of ID with features of brain regional GMVs. We found that patients with ID have higher interindividual variability than HCs. HYDRA identified two distinct and reliable neuroanatomical subtypes of ID. Two subtypes showed significantly different aberrance in GMVs compared with HCs. Specifically, subtype 1 exhibited widespread decreased GMVs in some brain regions, including the right inferior temporal gyrus, left superior temporal gyrus, left precuneus, right middle cingulate, and right supplementary motor area. Subtype 2 only demonstrated increased GMVs in the right superior temporal gyrus. Additionally, the GMVs of altered brain regions in subtype 1 were significantly correlated with daytime functioning, but in subtype 2, they were significantly correlated with sleep disturbance. These results explain conflicting neuroimaging findings and propose a potential objective neurobiological classification contributing to ID's precise clinical diagnosis and treatment. DATA AND CODE AVAILABILITY: The source and means of obtaining the data used in the study have been described fully in the Methods and Materials section. The codes and data in this study are available upon a reasonable request to the corresponding author.

Depression as a cardiovascular disorder: central-autonomic network, brain-heart axis, and vagal perspectives of low mood

If depressive symptoms are not caused by the physiological effects of a substance or other medical or neurological conditions, they are generally classified as mental disorders that target the central nervous system. However, recent evidence suggests that peripheral neural dynamics on cardiovascular control play a causal role in regulating and processing emotions. In this perspective, we explore the dynamics of the Central-Autonomic Network (CAN) and related brain-heart interplay (BHI), highlighting their psychophysiological correlates and clinical symptoms of depression. Thus, we suggest that depression may arise from dysregulated cardiac vagal and sympathovagal dynamics that lead to CAN and BHI dysfunctions. Therefore, treatments for depression should target the nervous system as a whole, with particular emphasis on regulating vagal and BHI dynamics.

Exploratory factor analysis of shared and specific genetic associations in depression and anxiety

BACKGROUND

Previous genetic studies of anxiety and depression were mostly based on independent phenotypes. This study aims to investigate the shared and specific genetic structure between anxiety and depression.

METHOD

To identify the underlying factors of Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and their combined scale (joint scale), we employed exploratory factor analysis (EFA) using the eigenvalue of parallel analysis. Subsequently, we conducted a genome-wide association study (GWAS) for these factors. In addition, we utilized LD Score Regression (LDSC) to determine the genetic correlations between the identified factors and four common mental disorders, three sleep phenotypes, and other traits that have been previously linked to anxiety and depression.

RESULTS

The EFA uncovered two factors for the GAD-7 scale, namely nervousness and disturbance, two factors for the PHQ-9 scale, namely negative affect and sleep/appetite disturbance, and four factors for the joint scale, specifically nervousness, anhedonia, sleep/appetite disturbance, and fidget. We identified two genome-wide significant genomic loci, with overlap across GAD-7 factor 1 and joint scale factor 1: rs148579586 (P_GAD-7 = 1.365 × 10^-09, P_{Joint scale} = 1.434 × 1^0-09) and rs201074060 (P_GAD-7 = 3.672 × 10^-09, P_{Joint scale} = 3.824 × 10^-09). Genetic correlations in factors ranged from 0.722 to 1.000 (all p < 1.786 × 10^-3) with 27 of 28 correlations being significantly smaller than one. The genetic correlations with external phenotypes showed small variation across the eight factors.

CONCLUSION

Unidimensional structures can provide more precise scores, which can aid in identifying the shared and specific genetic associations between anxiety and depression. This is a crucial step in characterizing the genetic structure of these conditions and their co-occurrence.

Mapping the neuroanatomical heterogeneity of OCD using a framework integrating normative model and non-negative matrix factorization

Obsessive-compulsive disorder (OCD) is a spectrum disorder with high interindividual heterogeneity. We propose a comprehensible framework integrating normative model and non-negative matrix factorization (NMF) to quantitatively estimate the neuroanatomical heterogeneity of OCD from a dimensional perspective. T1-weighted magnetic resonance images of 98 first-episode untreated patients with OCD and matched healthy controls (HCs, n = 130) were acquired. We derived individualized differences in gray matter morphometry using normative model and parsed them into latent disease factors using NMF. Four robust disease factors were identified. Each patient expressed multiple factors and exhibited a unique factor composition. Factor compositions of patients were significantly correlated with severity of symptom, age of onset, illness duration, and exhibited sex differences, capturing sources of clinical heterogeneity. In addition, the group-level morphological differences obtained with two-sample t test could be quantitatively derived from the identified disease factors, reconciling the group-level and subject-level findings in neuroimaging studies. Finally, we uncovered two distinct subtypes with opposite morphological differences compared with HCs from factor compositions. Our findings suggest that morphological differences of individuals with OCD are the unique combination of distinct neuroanatomical patterns. The proposed framework quantitatively estimating neuroanatomical heterogeneity paves the way for precision medicine in OCD.

Associations between specific depressive symptoms and psychosocial functioning in psychotherapy

BACKGROUND

Psychotherapy for depression aims to reduce symptoms and to improve psychosocial functioning. We examined whether some symptoms are more important than others in the association between depression and functioning over the course of psychotherapy treatment.

METHODS

We studied associations between specific symptoms of depression (PHQ-9) and change in social and occupational functioning (SOFAS), both with structural equation models (considering liabilities of depression and each specific symptom) and with logistic regression models (considering the risk for individual patients). The study sample consisted of adult patients (n = 771) from the Finnish Psychotherapy Quality Registry (FPQR) who completed psychotherapy treatment between September 2018 and September 2021.

RESULTS

Based on our results of logistic regression analyses and SEM models, the baseline measures of depression symptoms were not associated with changes in functioning. Changes in depressed mood or hopelessness, problems with sleep, feeling tired, and feeling little interest or pleasure were associated with improved functioning during psychotherapy. The strongest evidence for symptom-specific effects was found for the symptom of depressed mood or hopelessness.

LIMITATIONS

Due to our naturalistic study design containing only two measurement points, we were unable to study the causal relationship between symptoms and functioning.

CONCLUSIONS

Changes in certain symptoms during psychotherapy may affect functioning independently of underlying depression. Knowledge about the dynamics between symptoms and functioning could be used in treatment planning or implementation. Depressed mood or hopelessness appears to have a role in the dynamic relationship between depression and functioning.