How many different symptom combinations fulfil the diagnostic criteria for major depressive disorder? Results from the CRESCEND study

Identifying internalizing transdiagnostic profiles through motivational and cognitive control systems: Relations with symptoms, functionality, and quality of life

BACKGROUND

The diversity of patients' symptomatology among people seeking treatment on community-based mental health services poses significant challenges to traditional models of care. Recent approaches favor identifying transdiagnostic factors that allow a better understanding of patient heterogeneity and designing more effective and quality interventions. This study examines the heterogeneity of patients with internalizing symptoms based on profiles identified with cognitive and motivational control variables. Differences between these profiles on dimensional measures of psychopathology and quality of life are examined.

METHODS

263 patients were selected by non-probabilistic sampling procedures on mental health services in the province of Huelva (Spain). A latent class analysis on the standardized scale scores of The Behavioral Inhibition/Behavioral Activation System Scales and the Effortful Control Scale of the Adult Temperament Questionnaire Short-Form was conducted. Profiles were compared on the scores of the Inventory of Depression and Anxiety Symptoms-II, the WHO Disability Assessment Schedule II, and the Health Assessment Questionnaire SF-36.

RESULTS

The four latent profile solution is the one that showed the best fit indicators and substantive interpretability, with a kappa of 0.94 in the cross-validation procedure with 75% of the sample. No sex differences were found between the profiles (χ32 5.17, p = .160). Profiles #1 and #3, both characterized by an imbalance between low activation and high inhibition, had lower well-being, lower functionality, and quality of life. When comparing profile #2 (featuring the highest inhibitory control) lower scores on most internalizing scales are observed, specially claustrophobia, social anxiety, panic mania. Profile #4 (low control, high activation, and high inhibition) showed greater scores on both mania and euphoria and lower scores on emotional role.

CONCLUSIONS

We identified four distinctive profiles that had overly increased behavioral inhibition (as expected in internalizing disorders) and differed in the degree of imbalance between inhibition and activation systems, and between motivational systems and top-down cognitive control. The profile characterized by high activation and reduced cognitive (inhibitory) control was the one showing greater mood-related symptoms and lower levels of quality of life. These profiles could be generated by treatment providers to guide clinical management in an evidence-based manner.

The absurdity of the latent disease model in mental health: 10,130,814 ways to have a DSM-5-TR psychological disorder

BACKGROUND

Latent disease classification is currently the accepted approach to mental illness diagnosis. In the United States, this takes the form of the Diagnostic and Statistical Manual of Mental Disorders-5-Text Revision (DSM-5-TR). Latent disease classification has been criticized for reliability and validity problems, particularly regarding diagnostic heterogeneity. No authors have calculated the scope of the heterogeneity problem of the entire DSM-5-TR.

AIMS

We addressed this issue by calculating the unique diagnostic profiles that exist for every DSM-5-TR diagnosis.

METHODS

We did this by applying formulas previously used in smaller heterogeneity analyses to all diagnoses within the DSM-5-TR.

RESULTS

We found that there are 10,130,814 ways to be diagnosed with a mental illness using DSM-5-TR criteria. When specifiers are considered, this number balloons to over 161 septillion unique diagnostic presentations (driven mainly by bipolar II disorder). Additionally, there are 1,951,065 ways to present with psychiatric symptoms, yet not meet diagnostic criteria.

CONCLUSIONS

Latent disease classification leads to considerable heterogeneity in possible presentations. We provide examples of how latent disease classification harms research and treatment programs. We echo recommendations for the dismissal of latent disease classification as a mental illness diagnostic program.

Profiles of depressive symptoms in Peru: An 8-year analysis in population-based surveys

Background Profiles of depressive symptoms have been described due to heterogeneity in symptomatology and presentation. In our study, we estimate depressive symptom profiles and relate these symptom profiles to risk factors in the Peruvian population. Methods We carried out an observational study based on the Peruvian Demographic and Health Survey (2014-2022). Men and women aged 15 years and older living in urban and rural areas in all regions of Peru were included. The Patient Health Questionnaire-9 was used to define depressive symptom profiles. We estimated latent class models to define the profiles and performed a Poisson regression analysis to determine the associated factors. Results A total of 259,655 participants were included. The three-class model was found to be the most appropriate, and the classes were defined according to the severity of depressive symptoms (moderate-severe symptoms, mild symptoms, and without depressive symptoms). Also, it was found that the three classes identified have not changed during the years of evaluations, presenting very similar prevalence over the years. In addition, women are more likely than men to belong to a class with more severe depressive symptoms; and the older the age, the higher the probability of belonging to a class with greater severity of depressive symptoms. Conclusions Our study found that at the population level in Peru, depressive symptoms are grouped into three classes according to the intensity of the symptomatology present (no symptoms, mild symptoms and moderate-severe symptoms).

The contribution of the cingulate cortex: treating depressive symptoms in first-episode drug naïve schizophrenia

Background

Our previous study has shown the cingulate cortex abnormalities in first-episode drug naïve (FEDN) schizophrenia patients with comorbid depressive symptoms. However, it remains largely unknown whether antipsychotics may induce morphometric change in cingulate cortex and its relationship with depressive symptoms. The purpose of this study was to further clarify the important role of cingulate cortex in the treatment on depressive symptoms in FEDN schizophrenia patients.

Method

In this study, 42 FEDN schizophrenia patients were assigned into depressed patients group (DP, n = 24) and non-depressed patients group (NDP, n = 18) measured by the 24-item Hamilton Depression Rating Scale (HAMD). Clinical assessments and anatomical images were obtained from all patients before and after 12-week treatment with risperidone.

Results

Although risperidone alleviated psychotic symptoms in all patients, depressive symptoms were decreased only in DP. Significant group by time interaction effects were found in the right rostral anterior cingulate cortex (rACC) and other subcortical regions in the left hemisphere. After risperidone treatment, the right rACC were increased in DP. Further, the increasing volume of right rACC was negatively associated with improvement in depressive symptoms.

Conclusion

These findings suggested that the abnormality of the rACC is the typical characteristics in schizophrenia with depressive symptoms. It's likely key region contributing to the neural mechanisms underlying the effects of risperidone treatment on depressive symptoms in schizophrenia.

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

P50 sensory gating, cognitive deficits and depressive symptoms in first-episode antipsychotics-naïve schizophrenia

OBJECTIVE

Sensory gating P50 (SG-P50) may be involved in the pathophysiological mechanisms of impaired cognition in schizophrenia (SCZ). Comorbid depressive symptoms are common in SCZ patients and are also found to be associated with their cognitive impairment. However, it is unclear whether SG-P50 is abnormal in first episode antipsychotics naïve (FEAN) SCZ patients with depressive symptoms. Our aimed to investigate the relationships between SG-P50, depressive symptoms and neurocognition in FEAN-SCZ patients.

METHODS

We recruited 103 FEAN-SCZ patients (depression: n = 63; non-depression: n = 40) and 55 healthy controls. SG-P50 was measured using the standard auditory dual-click (S1&S2) paradigm. Clinical symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) and the Hamilton Depression Rating Scale-17 (HDRS-17). Cognitive performance was evaluated using the MATRICS Consensus Cognitive Battery (MCCB).

RESULTS

Compared with non-depressive patients, depressive patients had a significantly larger S2 amplitude (p = 0.005) and a higher S2/S1 ratio at trend level (p = 0.075) after corrected. There were significant differences in the scores of CPT-IP and Mazes (NAB) between depressive and non-depressive FEAN-SCZ patients (both p values < 0.05). For all patients, the SG-P50 S2/S1 ratio was significantly correlated with HDRS-17 score (r = 0.23, p = 0.020) and MCCB-Symbol coding (r = -0.16, p = 0.043). For depressive FEAN-SCZ patients, S2 amplitude was an independent predictor of the MCCB-Mazes (NAB) (β = -0.31, t = -2.52, p = 0.015).

CONCLUSIONS

SG-P50 deficit may be an informational biomarker for depressive symptoms and neurocognitive impairments in FEAN-SCZ patients.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

Correlates of suicidality in young people with depressive disorders: A systematic review

OBJECTIVE

Depression is one of the most prevalent and disabling mental health conditions among young people worldwide. The health and economic burdens associated with depressive illness are substantial. Suicide and depression are closely intertwined, yet a diagnosis of depression itself lacks predictive specificity for suicidal behaviour. To better inform suicide prevention and early intervention strategies for young people, improved identification of modifiable intervention targets is needed. The objective of this review was to identify clinical, psychosocial and biological correlates of suicidality in young people diagnosed with a broad range of unipolar and bipolar depressive disorders.

METHOD

Systematic searches were conducted across MEDLINE, Embase and PsycINFO to identify studies of young people aged 15-25 years diagnosed with unipolar or bipolar depressive disorders. An assessment of suicidality was required for inclusion. Reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 and Synthesis Without Meta-analysis guidelines.

RESULTS

We integrated findings from 71 studies including approximately 24,670 young people with clinically diagnosed depression. We identified 26 clinical, psychosocial and biological correlates of suicidality. Depression characteristics (type and severity), psychiatric comorbidity (particularly anxiety and substance use disorders) and neurological characteristics emerged as having the most evidence for being associated with suicidal outcomes. Our ability to pool data and conduct meaningful quantitative synthesis was hampered by substantial heterogeneity across studies and incomplete reporting; thus, meta-analysis was not possible.

CONCLUSION

Findings of this review reinforce the notion that suicidality is a complex phenomenon arising from the interplay of multiple contributing factors. Our findings question the utility of considering a diagnosis of depression as a specific risk factor for suicidality in young people. Suicidality itself is transdiagnostic; adoption of a transdiagnostic approach to investigating its aetiology and treatment is perhaps warranted. Future research investigating specific symptoms, or symptom networks, might help to further our understanding of suicidality among young people experiencing mental illness.

Metabolomic and inflammatory signatures of symptom dimensions in major depression

BACKGROUND

Major depressive disorder (MDD) is a highly heterogenous disease, both in terms of clinical profiles and pathobiological alterations. Recently, immunometabolic dysregulations were shown to be correlated with atypical, energy-related symptoms but less so with the Melancholic or Anxious distress symptom dimensions of depression in The Netherlands Study of Depression and Anxiety (NESDA) study. In this study, we aimed to replicate these immunometabolic associations and to characterize the metabolomic correlates of each of the three MDD dimensions.

METHODS

Using three clinical rating scales, Melancholic, and Anxious distress, and Immunometabolic (IMD) dimensions were characterized in 158 patients who participated in the Predictors of Remission to Individual and Combined Treatments (PReDICT) study and from whom plasma and serum samples were available. The NESDA-defined inflammatory index, a composite measure of interleukin-6 and C-reactive protein, was measured from pre-treatment plasma samples and a metabolomic profile was defined using serum samples analyzed on three metabolomics platforms targeting fatty acids and complex lipids, amino acids, acylcarnitines, and gut microbiome-derived metabolites among other metabolites of central metabolism.

RESULTS

The IMD clinical dimension and the inflammatory index were positively correlated (r = 0.19, p = 0.019) after controlling for age, sex, and body mass index, whereas the Melancholic and Anxious distress dimensions were not, replicating the previous NESDA findings. The three symptom dimensions had distinct metabolomic signatures using both univariate and set enrichment statistics. IMD severity correlated mainly with gut-derived metabolites and a few acylcarnitines and long chain saturated free fatty acids. Melancholia severity was significantly correlated with several phosphatidylcholines, primarily the ether-linked variety, lysophosphatidylcholines, as well as several amino acids. Anxious distress severity correlated with several medium and long chain free fatty acids, both saturated and polyunsaturated ones, sphingomyelins, as well as several amino acids and bile acids.

CONCLUSION

The IMD dimension of depression appears reliably associated with markers of inflammation. Metabolomics provides powerful tools to inform about depression heterogeneity and molecular mechanisms related to clinical dimensions in MDD, which include a link to gut microbiome and lipids implicated in membrane structure and function.

Digital phenotyping in depression diagnostics: Integrating psychiatric and engineering perspectives

Depression is a serious medical condition and is a leading cause of disability worldwide. Current depression diagnostics and assessment has significant limitations due to heterogeneity of clinical presentations, lack of objective assessments, and assessments that rely on patients' perceptions, memory, and recall. Digital phenotyping (DP), especially assessments conducted using mobile health technologies, has the potential to greatly improve accuracy of depression diagnostics by generating objectively measurable endophenotypes. DP includes two primary sources of digital data generated using ecological momentary assessments (EMA), assessments conducted in real-time, in subjects' natural environment. This includes active EMA, data that require active input by the subject, and passive EMA or passive sensing, data passively and automatically collected from subjects' personal digital devices. The raw data is then analyzed using machine learning algorithms to identify behavioral patterns that correlate with patients' clinical status. Preliminary investigations have also shown that linguistic and behavioral clues from social media data and data extracted from the electronic medical records can be used to predict depression status. These other sources of data and recent advances in telepsychiatry can further enhance DP of the depressed patients. Success of DP endeavors depends on critical contributions from both psychiatric and engineering disciplines. The current review integrates important perspectives from both disciplines and discusses parameters for successful interdisciplinary collaborations. A clinically-relevant model for incorporating DP in clinical setting is presented. This model, based on investigations conducted by our group, delineates development of a depression prediction system and its integration in clinical setting to enhance depression diagnostics and inform the clinical decision making process. Benefits, challenges, and opportunities pertaining to clinical integration of DP of depression diagnostics are discussed from interdisciplinary perspectives.

Machine Learning-Based Definition of Symptom Clusters and Selection of Antidepressants for Depressive Syndrome

The current polythetic and operational criteria for major depression inevitably contribute to the heterogeneity of depressive syndromes. The heterogeneity of depressive syndrome has been criticized using the concept of language game in Wittgensteinian philosophy. Moreover, “a symptom- or endophenotype-based approach, rather than a diagnosis-based approach, has been proposed” as the “next-generation treatment for mental disorders” by Thomas Insel. Understanding the heterogeneity renders promise for personalized medicine to treat cases of depressive syndrome, in terms of both defining symptom clusters and selecting antidepressants. Machine learning algorithms have emerged as a tool for personalized medicine by handling clinical big data that can be used as predictors for subtype classification and treatment outcome prediction. The large clinical cohort data from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D), Combining Medications to Enhance Depression Outcome (CO-MED), and the German Research Network on Depression (GRND) have recently began to be acknowledged as useful sources for machine learning-based depression research with regard to cost effectiveness and generalizability. In addition, noninvasive biological tools such as functional and resting state magnetic resonance imaging techniques are widely combined with machine learning methods to detect intrinsic endophenotypes of depression. This review highlights recent studies that have used clinical cohort or brain imaging data and have addressed machine learning-based approaches to defining symptom clusters and selecting antidepressants. Potentially applicable suggestions to realize machine learning-based personalized medicine for depressive syndrome are also provided herein.

Psychopathology and Neurocognition in the Era of the p-Factor: The Current Landscape and the Road Forward

Neurocognitive abilities have frequently been claimed to be involved in the aetiology of psychopathology. Neurocognitive deficits have been reported across many disorders, and theoretical perspectives associate these deficits to the onset and maintenance of the symptomology. Recently, the heterogeneity of symptoms, and comorbidity of disorders, have motivated the development of structural models of psychopathology. Structural models indicate that factors such as internalising, externalising, thought disorder and the p-factor account for a wide variety of symptomology. It is unclear how neurocognitive abilities are best examined within these structures to advance our understanding of psychopathology. In this paper, we use Caspi et al.’s seminal writings as a framework to describe how neurocognitive abilities have been previously associated with categorical disorders and recently associated, and claimed to drive, the factors of psychopathology. We discuss the implications of the p-factor as a substantive construct or statistical artefact, and how this impacts the exploration of neurocognitive abilities and psychopathology. Further, we provide the case for alternative structural approaches, describe an innovative hypothesis of neurocognitive functioning, the multidimensional hypothesis, and explain how this may further our understanding of the heterogeneity of neurocognitive performance and psychopathology at the individual level. Finally, we provide a road forward for the future examination of neurocognitive abilities in psychopathology.

Abnormal regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF) in first-episode drug-naïve schizophrenia patients comorbid with depression

The current study aimed to characterize the regional homogeneity (ReHo) or fractional amplitude of low frequency fluctuations (fALFF) alterations in first-episode drug-naïve schizophrenia comorbid with depression. Sixty-nine first-episode drug-naïve schizophrenia patients and 34 healthy controls (HC) were included in the final analysis. Schizophrenia patients were divided into depressive patients (DP) and non-depressive patients (NDP), with 35 and 34 patients respectively, using the Hamilton Rating Scale for Depression -17(HRSD-17). All participants underwent resting-state fMRI (rs-fMRI), the fALFF (slow-4 and slow-5 bands) and ReHo were used to process the data. The results revealed eleven brain regions with altered slow-5 fALFF, eleven brain regions with altered slow-4 fALFF and ten brain regions with altered ReHo among DP, NDP and HC groups. Compared to NDP, the DP group had increased slow-5 fALFF in the Right Inferior Temporal Gyrus, increased ReHo in the Right Superior and Inferior Frontal Gyrus. The altered slow-5 fALFF in the Right Inferior Temporal Gyrus, altered ReHo in the Right Inferior Frontal Gyrus and Superior Frontal Gyrus were all positively correlated with the depressive symptoms in patients. However, there were no significant differences in slow-4 fALFF between DP and NDP groups. Our results indicate that the increased slow-5 fALFF in the Right Inferior Temporal Gyrus, increased ReHo in the Right Superior and Inferior Frontal Gyrus were associated with depressive symptoms in schizophrenia, which may provide preliminary evidence in better understanding the neural mechanisms underlying depressive symptoms in schizophrenia.

An alternative approach to future diagnostic standards for major depressive disorder

During the period extending from 1780 to 1880, the conceptualization of melancholia changed from an intellectual to a mood model. The modern view of depression, based on Kraepelinian dualism, has reflected changes in opinion on psychiatric taxonomy of individual melancholia. From the point of view of an "operational revolution," the diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, 3rd edition (DSM-III) were based on a neoKraepelinian approach rooted in disease essentialism. In the revision process from the DSM-IV to the DSM-5, a combined dimensional and categorial approach was used. In the DSM-5, the diagnostic criteria for major depressive disorder are polythetic and operational in approach reflecting the heterogeneity of major depressive disorder. Although 227 different symptom combinations fulfilling the diagnostic criteria for major depressive disorder can be theoretically calculated, certain symptom combinations are more prevalent than others in real clinical situations. The heterogeneity of these operational criteria for major depressive disorder have been criticized in a manner informed by the Wittgensteinian analogy of the language game. Herein, our network analysis proposes a novel perspective on the psychopathology of major depressive disorder. The novel approach suggested here may lay the foundation for a redefinition of the traditional taxonomy of depression.

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.

Network Analysis of the Symptoms of Depressive Disorders Over the Course of Therapy: Changes in Centrality Measures

OBJECTIVE

Network analysis can be used in terms of a novel psychopathological approach for depressive syndrome. We aimed to estimate the successive network structures of depressive symptoms in patients with depressive disorder using data from the Clinical Research Center for Depression study.

METHODS

We enrolled 1,152 South Korean adult patients with depressive disorders who were beginning treatment for first-onset or recurrent depressive episodes. We examined the network structure of the severities of the items on the Hamilton Depression Rating Scale (HAMD) at baseline and at weeks 2, 12, 25, and 52. The node strength centrality of all the HAMD items at baseline and at week 2, 12, 25, and 52 in terms of network analysis.

RESULTS

In the severity networks, the anxiety (psychic) item was the most centrally situated in the initial period (baseline and week 2), while loss of weight was the most centrally situated item in the later period (weeks 25 and 52). In addition, the number of strong edges (i.e., edges representing strong correlations) increased in the late period compared to the initial period.

CONCLUSION

Our findings support a period-specific and symptom-focused therapeutic approach that can provide complementary information to the unidimensional total HAMD score.

Longitudinal changes of amplitude of low-frequency fluctuations in MDD patients: A 6-month follow-up resting-state functional magnetic resonance imaging study

OBJECTIVE

The aim of this study includes: (1) using resting-state functional magnetic resonance imaging (rsfMRI) to explore the aberrant brain regional spontaneous brain activities in acute major depressive disorder (MDD) patients; (2) to determine whether the abnormalities could be restored after 6 months of antidepressant treatment; (3) to investigate whether the differences in regional spontaneous brain activities are associated with clinical variables in MDD.

METHOD

RsfMRI scanning was performed in 149 MDD patients and 122 healthy control (HC) subjects at baseline. After 6 months of antidepressant treatment, rsfMRI scanning was reperformed in remitted MDD patients (MDD-R) (n=63). The characteristics of the amplitude of low-frequency fluctuations (ALFF), and the relationship between the fMRI representatives and clinical variables in the MDD group were analyzed.

RESULTS

(1) Compared to healthy controls, significantly decreased ALFF in the right precuneus/posterior cingulate cortex (PCUN/PCC) was detected in MDD. (2) The ALFF value of precuneus in MDD-R group did not change significantly after a 6-month antidepressant treatment and was still lower than the HC group when remission was achieved (P = 0.002). (3) No correlations were found between ALFF in the right PCUN/PCC and Hamilton Depression Rating Scale(HAMD) total score, illness duration, age of onset, and the number of episodes in the baseline MDD group. The ALFF change was not correlated with depressive symptom improvement in MDD-R group.

CONCLUSIONS

The reduction of ALFF in the precuneus persisted in MDD who achieved clinical remission, suggesting that the decreased ALFF in PCUN/PCC may be a trait marker of MDD.

The definition and measurement of heterogeneity

Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case-control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system's event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures-largely developed for economic and ecological research-to be useful in modern translational psychiatric science.

The Centrality of Depression and Anxiety Symptoms in Major Depressive Disorder Determined Using a Network Analysis

BACKGROUND

Comorbid anxiety symptoms are highly prevalent and closely linked with poorer treatment outcomes, chronicity, and hospitalization in major depressive disorder (MDD). Our study aimed to estimate the network of depression and anxiety symptoms that was developed based on a sample of MDD patients.

METHODS

We constructed a network of the 21 Beck Depression Inventory (BDI) symptoms and 21 Beck Anxiety Inventory (BAI) symptoms in 223 patients with MDD who were beginning psychiatric treatment. In addition, each of the depression and anxiety symptoms was considered to be an ordered categorical variable ranging in value from 0 to 3.

RESULTS

The three depression symptoms including loss of energy, loss of interest, and worthlessness and the seven anxiety symptoms including faintness or lightheadedness, feeling of choking, feeling scared, fear of the worst happening, nervousness, inability to relax, and feeling shaky were identified as the ten most central nodes within a network of depression and anxiety symptoms. The inter-connection between irritability and nervousness was a strong trans-diagnostic edge within the network of depression and anxiety symptoms.

LIMITATIONS

Because our study was designed in a cross-sectional manner, the networks were estimated undirectionally.

CONCLUSIONS

Our findings show that depression symptoms are not more central than anxiety symptoms within an estimated network structure of symptoms in patients with MDD. Moreover, the inter-connection between irritability and nervousness may suggests a probable trans-diagnostic association in MDD symptomatology.

Network analysis of the depressive symptom profiles in Asian patients with depressive disorders: Findings from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD)

AIM

We aimed to estimate the network structures of depressive symptoms using network analysis and evaluated the geographic regional differences in theses network structures among Asian patients with depressive disorders.

METHODS

Using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants (REAP-AD), the network of the ICD-10 diagnostic criteria for depressive episode was estimated from 1174 Asian patients with depressive disorders. The node strength centrality of all ICD-10 diagnostic criteria for a depressive episode was estimated using a community-detection algorithm. In addition, networks of depressive symptoms were estimated separately among East Asian patients and South or Southeast Asian patients. Moreover, networks were estimated separately among Asian patients from high-income countries and those from middle-income countries.

RESULTS

Persistent sadness, fatigue, and loss of interest were the most centrally situated within the network of depressive symptoms in Asian patients with depressive disorders overall. A community-detection algorithm estimated that when excluding psychomotor disturbance as an outlier, the other nine symptoms formed the largest clinically meaningful cluster. Geographic and economic variations in networks of depressive symptoms were evaluated.

CONCLUSION

Our findings demonstrated that the typical symptoms of the ICD-10 diagnostic criteria for depressive episode are the most centrally situated within the network of depressive symptoms. Furthermore, our findings suggested that cultural influences related to geographic and economic distributions of participants could influence the estimated depressive symptom network in Asian patients with depressive disorders.

Symptom-based selection of antidepressants

Most evidence-based pharmacological guidelines recommend selective serotonin reuptake inhibitors, serotoninnorepinephrine reuptake inhibitors, norepinephrine-dopamine reuptake inhibitors or norepinephrine and specific serotonin antidepressants as the first-line treatment for major depression. Since the clinical factors associated with treating patients with depression are relatively complex, it can be challenging to apply the recommendations of evidence-based medicine verbatim. Furthermore, the diagnostic criteria of major depressive disorders, which are defined in a polythetic and operational manner, inevitably result in their heterogeneity. Studies have inferred that depressive syndrome may be connected with “family resemblance” rather than being shared with a neurobiological essence. Therefore, the symptom-based selection of antidepressants can be supported by a network analysis that provides a novel perspective on the symptom structure of major depression. The symptom-based treatment algorithm suggests treatment options that can be applied to the symptoms that are included in and excluded from the diagnosis criteria of major depressive disorder. The symptom-based selection of antidepressants and other psychotropic agents involves matching the deconstructed symptoms of depression to the specific neuroanatomical regions and neurotransmitters. This ensures timely and optimized treatment options for patients with depression.

Metabolic profiling identifies TC and LDL as potential serum biomarkers for depressive symptoms in schizophrenia

This study aimed to explore the relationship between serum levels of cardiometabolic biomarkers and depressive symptoms in schizophrenia patients treated with atypical antipsychotics. A total of 210 patients with schizophrenia and 70 healthy controls were recruited in our present study. All patients were rated on the 17-item Hamilton Depression Rating Scale (HAMD-17) to measure depressive symptoms and the Positive and Negative Syndrome Scale (PANSS) for psychopathology. Serum cardiometabolic biomarkers (HDL, LDL, TC, TG, GLU) in all participants were measured. Our results showed that schizophrenia patients had higher levels of serum GLU, TG, TC, LDL and BMI, but lower levels of HDL than controls (all P < 0.05). Compared to patients without depressive symptoms, those with depressive symptoms showed higher PANSS total, general psychopathology, positive and negative symptom scores (all p < 0.05), as well as higher serum levels of LDL (p < 0.001) and TC (p = 0.011). In addition, our correlation analysis showed that serum LDL (P < 0.001) and TC (P = 0.045) levels were positively associated with HAMD total scores in schizophrenia patients after age, sex and education levels were controlled. Our results suggest the appearance of depression in schizophrenia patients may be associated with high levels of metabolic parameters, especially TC and LDL.

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.

Classification of Psychiatric Disorders

Because of the poor link between psychiatric diagnosis and neurobiological findings, it is difficult to classify mental disorders. The changes made to psychiatric diagnostic systems over the years can be understood in terms of "practical conservatism." The Diagnostic and Statistical Manual of Mental Disorders (DSM)-I and DSM-II were theoretically supported by the psychoanalytic and psychodynamic approach. Subsequently, psychiatric diagnoses of this kind were opposed by the anti-psychiatry movement, as well as by the findings of the Rosenhan experiment. Thus, the DSM-III revolution contained more empiricism, aligning psychiatry with biomedicine. Psychiatric diagnoses are classified and defined in terms of Kraepelinian dualism, using a categorical approach. The empirical trend was continued in the DSM-IV. To overcome the limitations of current psychiatric diagnostic systems and integrate fundamental genetic, neurobiological, behavioral, environmental, and experimental components into psychiatry, the Research Domain Criteria (RDoC) were established. To overcome the limitations of the categorical approach, psychiatrists have considered adopting a dimensional approach. However, their efforts were frustrated in the DSM-5 revision process. Thus, the DSM-5 is characterized by the rearrangement of psychiatric diagnoses, the partial adoption of a dimensional approach, the introduction of new diagnoses, and harmonization with the International Classification of Diseases.

Bifrontal active and sham rTMS in treatment-resistant unipolar major depression

BACKGROUND AND AIM

Prevention of the recurrence of major depression and its residual symptoms requires effective treatment. Our aim was to study the effects of bifrontal active rTMS controlled by sham rTMS in treatment-resistant unipolar major depressive disorder (MDD).

METHODS

Thirty-seven patients with treatment-resistant MDD were randomized into two groups. One group received a total of 30 sessions of active bifrontal rTMS (10 Hz rTMS on left dorsolateral prefrontal cortex (DLPFC) and 1 Hz rTMS on right DLPFC) and the other group received bilateral sham rTMS on five days a week for six weeks.

RESULTS

Depressive symptoms significantly improved in both the groups, but without a significant group difference. Furthermore, patients with psychotic depression improved similarly to those with moderate or severe depression.

CONCLUSIONS

The results of present study indicate a large sham effect of stimulation treatment. The intensive structured treatment protocol may explain the positive outcome in both the groups. It is important to recognize, appreciate, and utilize placebo effects as a significant means of rehabilitation in psychiatric care.

Remission rate of transcranial magnetic stimulation compared with electroconvulsive therapy: a case-control study

PURPOSE

To compare the rate of remission, rate of response, change in depressive symptoms, and adverse effects between repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT).

MATERIALS AND METHODS

In this retrospective case-control study, 35 patients treated for depression with rTMS (left dorsolateral prefrontal cortex, 90% observed motor threshold, 10 Hz, 2000 pulses/session, 15 sessions) at Örebro University Hospital, Sweden (cases), were compared with a matched group of 35 patients treated for depression with ECT (controls). Data on controls were obtained from the Swedish National Quality Register for ECT (Q-ECT). Severity of depression was evaluated using the Montgomery-Åsberg Depression rating scale (MADRS).

RESULTS

Remission rate was 26% for cases and 43% for controls (p = .3). Response rate was 40% for cases and 51% for controls (p = .63). The median decrease in MADRS was 11 (IQR 3-19) vs. 17 (IQR 6-27; p = .10) for rTMS and ECT, respectively. There was no statistically significant difference in any measure of treatment effect between rTMS and ECT. More than half of the patients of the rTMS group experienced scalp discomfort and 11% of the ECT group had memory disturbances.

CONCLUSIONS

All measures of therapeutic efficacy were numerically inferior in the rTMS group compared to the ECT group. The differences were not statistically significant, probably because the sample size was small. More studies are required to find the optimal place for rTMS within the Swedish health care system. Such studies could be facilitated by inclusion of rTMS in the Q-ECT.

The association between mean platelet volume levels and poststroke depression

OBJECTIVE

High levels of mean platelet volume (MPV) had been found in depression subjects. We sought to examine the relationship between MPV and poststroke depression (PSD).

METHODS

One hundred and eighty-five patients with acute ischemic stroke were enrolled in our study. Peripheral venous blood samples were drawn at admission and MPV levels were measured by the automated hematology analyzer. Patients with a HAMD-17 score >7 were diagnosed as having PSD.

RESULTS

We found that 60 patients (32.4%) developed PSD, the MPV levels in PSD patients were significantly higher (9.3 ± 1.8 fl) compared to non-PSD patients (8.5 ± 1.6 fl). High MPV levels (≥9.1 fl) were independently correlated with PSD (OR 2.762, 95% CI 1.138-6.702, p = 0.025).

CONCLUSIONS

Patients with higher levels of MPV at admission were correlated with the development of PSD at 1 month after stroke and might be a predictor of its presence.