A Critical Evaluation of Dynamical Systems Models of Bipolar Disorder

Bipolar disorder (BD) is a mood disorder involving recurring (hypo)manic and depressive episodes. The inherently temporal nature of BD has inspired its conceptualization using dynamical systems theory, which is a mathematical framework for understanding systems that evolve over time. In this paper we provide a critical review of dynamical systems models of BD. Owing to heterogeneity of methodologies and experimental designs in computational modeling, we designed a structured approach to guide our review in a fashion that parallels the appraisal of animal models by their Face, Predictive, and Construct Validity. This tool, the Validity Appraisal Guide for Computational Models (VAG-CM) is not an absolute estimate of validity, but rather a guide for more objective appraisal of models in this review. We identified 26 studies published before November 18, 2021 that proposed generative dynamical systems models of time-varying signals in BD. Two raters independently applied the VAG-CM to included studies, obtaining a mean Cohen's kappa of 0.55 (95% CI [0.45, 0.64]) prior to establishing consensus ratings. Consensus VAG-CM ratings revealed three model/study clusters: data-driven models with face validity, theory-driven models with predictive validity, and theory-driven models lacking all forms of validity. We conclude that future models should be developed using a hybrid approach that first operationalizes BD features of interest using empirical data (a data-driven approach), followed by explanations of those features using generative models with components that are homologous to physiological or psychological systems involved in BD (a theory-driven approach).

Use of 30-Hz Accelerated iTBS in Drug-Resistant Unipolar and Bipolar Depression in a Public Healthcare Setting: A Case Series

Background: Depressive episodes, especially when resistant to pharmacotherapy, are a hard challenge to face for clinicians and a leading cause of disability worldwide. Neuromodulation has emerged as a potential therapeutic option for treatment-resistant depression (TRD), in particular transcranial magnetic stimulation (TMS). In this article, we present a case series of six patients who received TMS with an accelerated intermittent theta-burst stimulation (iTBS) protocol in a public healthcare setting.Methods: We enrolled a total number of six participants, affected by a treatment-resistant depressive episode, in either Major Depressive Disorder (MDD) or Bipolar Disorder (BD). Patients underwent an accelerated iTBS protocol, targeted to the left dorsolateral prefrontal cortex (DLPFC), 3-week-long, with a total of 6 days of overall stimulation. On each stimulation day, the participants received 3 iTBS sessions, with a 15-min pause between them. Patients were assessed by the Hamilton Rating Scale for Depression (HAM-D), the Montgomery-Asberg Depression Rating Scale (MADRS), the Hamilton Rating Scale for Anxiety (HAM-A), and the Mania Rating Scale (MRS). At baseline (T0), at the end of the second week (T1), and at the end of the cycle of stimulation (T2).Results: The rANOVA (repeated Analysis of Variance) statistics showed no significant effect of time on the rating scale scores, with a slight decrease in MADRS scores and a very slight increase in HAM-A and HAM-D scores. No manic symptoms emerged during the entire protocol.Conclusions: Although accelerated iTBS might be considered a less time-consuming strategy for TMS administration, useful in a public healthcare setting, our results in a real-word six-patient population with TRD did not show a significant effect. Further studies on wider samples are needed to fully elucidate the potential of accelerated iTBS protocols in treatment-resistant depression.

Latent Markov Factor Analysis: A Mixture Modeling Approach for Evaluating Within- and Between-Person Measurement Model Differences in Intensive Longitudinal Data

SUMMARY DOCTORAL DISSERTATION: Experience sampling methodology, in which participants are repeatedly questioned via smartphone apps, is popular for studying psychological constructs or “factors” (e.g., well-being or depression) within persons over time. The validity of such studies (e.g., concerning treatment decisions) may be hampered by distortions of the measurement of the relevant constructs due to response styles or item interpretations that change over time and differ across persons. In this PhD project, we developed a new approach to evaluate person- and time-point-specific distortions of the construct measurements, taking into account the specific characteristics of (time-intensive) longitudinal data inherent to experience sampling studies. Our new approach, latent Markov factor analysis, extends mixture factor analysis and clusters time-points within persons according to their factor model. The factor model describes how well items measure the constructs. With the new approach, researchers can examine how many and which factor models underlie the data, for which persons and time-points they apply, and thus which observations are validly comparable. Such insights can also be interesting in their own right. In personalized healthcare, for example, detecting changes in response styles is critical for accurate decisions about treatment allocation over time, as response styles may be related to the occurrence of depressive episodes.

The Tryptophan Catabolite or Kynurenine Pathway’s Role in Major Depression

Kynurenine or tryptophan catabolite (TRYCAT) pathway contributes to the pathophysiology of major depression disorder (MDD) and major depressive episodes (MDE) in bipolar disorder and suicidal behaviors. The consequences of the overactivation of this pathway large reduced tryptophan (TRP) levels in peripheral blood and the CNS and increased levels of neurotoxic TRYCATs including kynurenine (KYN), 3-hydroxy kynurenine (3HK), quinolinic acid (QA), xanthurenic acid (XA), and picolinic acid (PA). However, other TRYCATs are protective, such as kynurenic acid (KA) and anthranilic acid (AA). Inflammation and cell-mediated immune activation along with oxidative and nitrosative stress (O&NS) may stimulate the first and rate-limiting enzyme of this pathway, namely indoleamine-2,3-dioxygenase (IDO). Therefore, during depression, balancing neuroprotective versus neurotoxic TRYCATs and balancing activation of the immune response system (IRS) versus the compensatory immune response system is crucial for achieving better treatment outcomes. Furthermore, targeting the causes of TRYCAT pathway activation (immune activation and O&NS) is probably the most effective strategy to treat depression. In the present review, we aim to provide a comprehensive explanation of the impact of TRYCATs in terms of pathophysiology and treatment of MDD and MDE.

Lifetime use of MDMA/ecstasy and psilocybin is associated with reduced odds of major depressive episodes

Background: Depression is a major mental health issue worldwide, with high rates of chronicity and non-recovery associated with the condition. Existing treatments such as antidepressant medication and psychological treatments have modest effectiveness, suggesting the need for alternative interventions. Aim: The aim of this study was to examine the relationships between MDMA (3,4-methylenedioxymethamphetamine)/ecstasy and psilocybin use and major depressive episodes (MDEs). Methods: This observational study used data from a large ( N = 213,437) nationally representative sample of US adults to test the association of lifetime use of MDMA/ecstasy, psilocybin and other classic psychedelics (lysergic acid diethylamide (LSD), peyote, mescaline), other illegal substances (e.g. cocaine, phencyclidine (PCP)), and legal/medicinal substances of misuse (e.g. pain relievers, tranquilizers) with lifetime, past year, and past year severe MDEs. Results: Results revealed that lifetime MDMA/ecstasy use was associated with significantly lowered odds of a lifetime MDE (adjusted odds ratio (aOR) = 0.84; p < 0.001), past year MDE (aOR = 0.84; p < 0.001), and past year severe MDE (aOR = 0.82; p < 0.001). Psilocybin was associated with significantly lowered odds of a past year MDE (aOR = 0.90; p < 0.05) and past year severe MDE (aOR = 0.87; p < 0.05). All other substances either shared no relationship with a MDE or conferred increased odds of an MDE. Conclusions: These results suggest that MDMA/ecstasy and psilocybin use is associated with lower risk of depression. Experimental studies are needed to test whether there is a causal association between use of these compounds and the alleviation of depressive symptoms.

Status of Medications Prescribed for Psychiatric Disorders in Korean Pediatric and Adolescent Patients

While mental health services for children are increasing, few psychiatric drugs have been approved for such use. We analyzed claim data from 19,557 South Korean pediatric and adolescent patients (<20 years) who were diagnosed with schizophrenia, bipolar disorder, major depressive disorder, anxiety disorder, attention deficit-hyperactivity disorder (ADHD), or a tic disorder. Among these diseases, depressive episodes were the most common, followed by an anxiety disorder, ADHD, bipolar disorder, tic disorder, and schizophrenia. For each disease, prescriptions were categorized as full-label (approved indication with pediatric dosing in the package insert (PI)), partial-label (approved indication without pediatric dosing in the PI), and contraindication (contraindicated for the specific pediatric age in the PI). For schizophrenia, major depressive disorder, and anxiety disorder, more than 50% of the patients were prescribed partial-labeled medications. Additionally, more than 5% of patients with major depressive disorder were prescribed medications that were contraindicated for their age group. Our findings reveal that children with full-labeled psychiatric conditions are commonly administered drugs that are not explicitly approved for either their disease state or age, including off-label and unlicensed drugs. To use pharmaceuticals more safely, expanding drug indications using real-world data are needed.

A Hidden Link between Subclinical Hypothyroidism and Depression: A literature Review

The association between Subclinical hypothyroidism and Depression is recognised. It is found that patients with Thyroid disorders are more prone to develop depressive symptoms and depression may be accompanied by various subtle thyroid abnormalities. The most commonly documented abnormalities are elevated T4 levels, Low T3, elevated rT3, a blunted TSH response to TSH, Positive anti thyroid autoantibodies and elevated CSF TRH concentrations. It is also found that thyroid hormone supplements appear to accelerate and enhance the clinical response to antidepressants. It is found out that Depression is associated with changes in Hypothalamic-pituitary axis as thyroid hormones act on the central nervous system. Mild thyroid dysfunction causes depression in younger patients (<60 years old) diagnosed by depressive scale. It was found that differences in age group may cause depressive episodes. Depressive episodes such as anxiety and the risk of committing suicide are considerable factors that differ according to the age of the individuals.SCH was found to be associated with depression in the younger adults (<60 years old). The only difference between SCH and normal thyroid function is TSH.In depressive disorder and subclinical hypothyroidism sex differences have also been recognised. Association between subclinical hypothyroidism and Depression is assessed by various depressive scores such as Beck Depression Inventory and Hamilton depression rating scale. As Subclinical hypothyroidism is associated with low mood, Serum levels of TSH, FT3, FT4 and Hamilton depression, treatment with Levothyroxine showed significant decrease is TSH levels and Hamilton scores were decreased. Since the prevalence of depressive symptoms in hypothyroidism is high TSH cut-off levels is used,TSH cut off value for hypothyroidism is based on associated symptoms,TSH cut-off value is 2.5 MIU/L is optimal

Association Between Frontal Alpha Asymmetry With Cognitive Symptoms, Depression Severity, and Insomnia

Objective: Previous studies have compared depressive episodes between bipolar disorder (BD) and major depressive disorder (MDD) using quantitative electroencephalogram (QEEG); however, there are no distinct discriminating feature between them. Here, we used QEEG to directly compare the alpha asymmetry and absolute power of each band between patients with BD and MDD.Methods: Fifty in-patients with major depressive episodes between 2019 and 2021 were retrospectively enrolled. Self-reported questionnaires including the Beck Depression Inventory (BDI), Korean version of the Childhood Trauma Questionnaire, and Adult Attention-Deficit/Hyperactivity Disorder Self Report Scale (ASRS) were used to evaluate the symptoms. The absolute power of QEEG delta, theta, alpha, beta, high beta waves, and the Z-scores of frontal alpha asymmetry were collected. A t-test and Pearson’s correlation test were conducted using these data and based on these results, an analysis of covariance was conducted.Results: There were no significant differences between MDD and BD in QEEG power or alpha asymmetry. Patients with severe depression (BDI ≥29) had higher alpha power at FP1 (p=0.037), FP2 (p=0.028), F3 (p=0.047), F4 (p=0.016), and higher right frontal alpha asymmetry at F3–F4 (p=0.039). Adult patients with features consistent with ADHD (ASRS ≥4) had higher right frontal alpha asymmetry at F3–F4 (p=0.046). Patients with insomnia had higher left frontal alpha asymmetry at F3–F4 (p=0.003).Conclusion: QEEG limited the differential diagnosis of MDD and BD. However, frontal alpha asymmetry did exist in depression and affected cognitive impairment, insomnia, and depression severity in particular. Future studies with improved methodologies are needed for a better comparison.

Digital Phenotype of Mood Disorders: a conceptual and critical review (Preprint)

BACKGROUND Mood disorder is commonly diagnosed and staged using clinical features that rely merely on subjective data. The concept of digital phenotyping is based on the idea that collecting real-time markers of human behavior allow one to determine the "digital signature of a pathology". This strategy assumes that behaviors are "quantifiable" from data extracted and analyzed through digital sensors, wearable devices or smartphones. That concept could bring a shift for the diagnosis of mood disorder, introducing for the first time paraclinical testing on psychiatric routine care. OBJECTIVE The main objective of this review was to propose a conceptual and critical review of the literature regarding the theoretical and technical principles of digital phenotypes applied to mood disorders. METHODS We conducted a selective review of the literature by updating a previous article and querying the PubMed database between February 2017 and November 2021 on titles with the relevant keywords regarding digital phenotyping, mood disorders and artificial intelligence. RESULTS 858 articles were included for evaluation, 43 articles were taken into account and classified by data source (multimodal, actigraphy, ECG, smartphone use, voice analysis, body temperature). For depressive episodes, the main finding is the decrease in terms of functional and biological parameters (decrease in activities and walking, decrease in the number of calls and SMS, decrease in temperature and HRV) while the manic phase produces the reverse phenomenon (increase in activities, number of calls and HRV). CONCLUSIONS The various studies presented support the potential interest in digital phenotyping to computerize the clinical characteristics of mood disorders

A Hidden Link between Subclinical Hypothyroidism and Depression: A literature Review

The association between Subclinical hypothyroidism and Depression is recognised. It is found that patients with Thyroid disorders are more prone to develop depressive symptoms and depression may be accompanied by various subtle thyroid abnormalities. The most commonly documented abnormalities are elevated T4 levels, Low T3, elevated rT3, a blunted TSH response to TSH, Positive anti thyroid autoantibodies and elevated CSF TRH concentrations. It is also found that thyroid hormone supplements appear to accelerate and enhance the clinical response to antidepressants. It is found out that Depression is associated with changes in Hypothalamic-pituitary axis as thyroid hormones act on the central nervous system. Mild thyroid dysfunction causes depression in younger patients (<60 years old) diagnosed by depressive scale. It was found that differences in age group may cause depressive episodes. Depressive episodes such as anxiety and the risk of committing suicide are considerable factors that differ according to the age of the individuals.SCH was found to be associated with depression in the younger adults (<60 years old). The only difference between SCH and normal thyroid function is TSH.In depressive disorder and subclinical hypothyroidism sex differences have also been recognised. Association between subclinical hypothyroidism and Depression is assessed by various depressive scores such as Beck Depression Inventory and Hamilton depression rating scale. As Subclinical hypothyroidism is associated with low mood, Serum levels of TSH, FT3, FT4 and Hamilton depression, treatment with Levothyroxine showed significant decrease is TSH levels and Hamilton scores were decreased. Since the prevalence of depressive symptoms in hypothyroidism is high TSH cut-off levels is used,TSH cut off value for hypothyroidism is based on associated symptoms,TSH cut-off value is 2.5 MIU/L is optima