Smartphone-based interventions in bipolar disorder: Systematic review and meta-analyses of efficacy. A position paper from the International Society for Bipolar Disorders (ISBD) Big Data Task Force.

BACKGROUND: The clinical effects of smartphone-based interventions for bipolar disorder (BD) have yet to be established. OBJECTIVES: To examine the efficacy of smartphone-based interventions in BD and how the included studies reported user-engagement indicators. METHODS: We conducted a systematic search on January 24, 2022, in PubMed, Scopus, Embase, APA PsycINFO, and Web of Science. We used random-effects meta-analysis to calculate the standardized difference (Hedges' g) in pre-post change scores between smartphone intervention and control conditions. The study was pre-registered with PROSPERO (CRD42021226668). RESULTS: The literature search identified 6034 studies. Thirteen articles fulfilled the selection criteria. We included seven RCTs and performed meta-analyses comparing the pre-post change in depressive and (hypo)manic symptom severity, functioning, quality of life, and perceived stress between smartphone interventions and control conditions. There was significant heterogeneity among studies and no meta-analysis reached statistical significance. Results were also inconclusive regarding affective relapses and psychiatric readmissions. All studies reported positive user-engagement indicators. CONCLUSION: We did not find evidence to support that smartphone interventions may reduce the severity of depressive or manic symptoms in BD. The high heterogeneity of studies supports the need for expert consensus to establish ideally how studies should be designed and the use of more sensitive outcomes, such as affective relapses and psychiatric hospitalizations, as well as the quantification of mood instability. The ISBD Big Data Task Force provides preliminary recommendations to reduce the heterogeneity and achieve more valid evidence in the field.

Precision non-implantable neuromodulation therapies: a perspective for the depressed brain

Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.

Precision non-implantable neuromodulation therapies: a perspective for the depressed brain.

Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more "precision-oriented" practice.

Socio-demographic and psychiatric risk factors in incident and persistent depression: An analysis in the occupational cohort of ELSA-Brasil.

BACKGROUND: Depression is a main source of disability worldwide. Identifying risk factors associated with incident and persistent episodes could inform clinical practice and hence mitigate their burden. However, previous research has focused on populations from developed countries. Thus, we evaluated sociodemographic risk factors and psychiatric comorbidities associated with incident and persistent depression in a large Brazilian occupational cohort. METHODS: We examined baseline (2008-2010, n = 15,105) and follow-up (2012-2014) data from the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Based on the presence of depression diagnosis at two timepoints, we diagnosed persistent and incident depression. Simple and multiple logistic regression analyses were employed to explore risk factors associated with incident and persistent depression. As gender is associated with the exposure and outcome variables, analyses stratified by gender were also conducted. RESULTS: Presence of any anxiety disorder, obsessive-compulsive disorder, and female gender were significant (p < 0.001) risk factors for depression incidence (odds ratios of 2.59, 3.6, and 1.82, respectively) and persistence (odds ratios of 6.94, 14.37, and 2.85, respectively) in multiple models, whereas having university degree decreased the odds of depression incidence (0.74) and persistence (0.45). In stratified analyses, the effects of low education were only evident in women. LIMITATIONS: Brief depressive episodes could not be measured by our assessments. CONCLUSION: In this occupational cohort, female gender, low education and psychiatric comorbidities were associated with unfavorable depression courses. Interventions targeting comorbidities could prevent depression incidence and persistence.

Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force.

OBJECTIVES: The International Society for Bipolar Disorders Big Data Task Force assembled leading researchers in the field of bipolar disorder (BD), machine learning, and big data with extensive experience to evaluate the rationale of machine learning and big data analytics strategies for BD. METHOD: A task force was convened to examine and integrate findings from the scientific literature related to machine learning and big data based studies to clarify terminology and to describe challenges and potential applications in the field of BD. We also systematically searched PubMed, Embase, and Web of Science for articles published up to January 2019 that used machine learning in BD. RESULTS: The results suggested that big data analytics has the potential to provide risk calculators to aid in treatment decisions and predict clinical prognosis, including suicidality, for individual patients. This approach can advance diagnosis by enabling discovery of more relevant data-driven phenotypes, as well as by predicting transition to the disorder in high-risk unaffected subjects. We also discuss the most frequent challenges that big data analytics applications can face, such as heterogeneity, lack of external validation and replication of some studies, cost and non-stationary distribution of the data, and lack of appropriate funding. CONCLUSION: Machine learning-based studies, including atheoretical data-driven big data approaches, provide an opportunity to more accurately detect those who are at risk, parse-relevant phenotypes as well as inform treatment selection and prognosis. However, several methodological challenges need to be addressed in order to translate research findings to clinical settings.

Adjunctive tianeptine treatment for bipolar disorder: A 24-week randomized, placebo-controlled, maintenance trial.

OBJECTIVE: The purpose of this study was to assess the efficacy and tolerability of tianeptine as an adjunctive maintenance treatment for bipolar depression. METHODS: This is a multicenter double-blind randomized placebo-controlled maintenance trial of adjunctive tianeptine 37.5 mg/day. Participants ( n=161) had a Montgomery-Asberg Depression Rating Scale ⩾12 at entry. After eight weeks of open-label tianeptine treatment, those who responded to tianeptine ( n=69) were randomized to adjunctive tianeptine ( n=36) or placebo ( n=33) in addition to usual treatment. Kaplan-Meier estimates and the Mantel-Cox log-rank test were used to evaluate differences in time to intervention for a mood episode between the tianeptine and placebo groups. We also assessed overall functioning, biological rhythms, quality of life, rates of manic switch and serum brain-derived neurotrophic factor levels. RESULTS: There were no differences between adjunctive tianeptine or placebo regarding time to intervention or depression scores in the 24-week double-blind controlled phase. Patients in the tianeptine group showed better performance in the letter-number sequencing subtest from the Wechsler Adult Intelligence Scale at the endpoint ( p=0.014). Tianeptine was well tolerated and not associated with higher risk for manic switch compared to placebo. CONCLUSION: Tianeptine was not more effective than placebo in the maintenance treatment of bipolar depression. There is preliminary evidence suggesting a pro-cognitive effect of tianeptine in working memory compared to placebo.

Revisiting the Werther Effect in the 21st Century: Bullying and Suicidality Among Adolescents Who Watched 13 Reasons Why.

Unlike most leading causes of death in the United States, suicide rates have not declined during the past 50 years.1 Among young people the situation is even more dramatic, because suicide rates are rising,2 and suicide is now the second cause of death in 15- to 29-year-olds globally.3 It has been suggested that descriptions of suicide in the media might affect behavior and that the young might be more vulnerable to this effect.4.

Virginia Woolf, neuroprogression, and bipolar disorder

Family history and traumatic experiences are factors linked to bipolar disorder. It is known that the lifetime risk of bipolar disorder in relatives of a bipolar proband are 5-10% for first degree relatives and 40-70% for monozygotic co-twins. It is also known that patients with early childhood trauma present earlier onset of bipolar disorder, increased number of manic episodes, and more suicide attempts. We have recently reported that childhood trauma partly mediates the effect of family history on bipolar disorder diagnosis. In light of these findings from the scientific literature, we reviewed the work of British writer Virginia Woolf, who allegedly suffered from bipolar disorder. Her disorder was strongly related to her family background. Moreover, Virginia Woolf was sexually molested by her half siblings for nine years. Her bipolar disorder symptoms presented a pernicious course, associated with hospitalizations, suicidal behavioral, and functional impairment. The concept of neuroprogression has been used to explain the clinical deterioration that takes places in a subgroup of bipolar disorder patients. The examination of Virgina Woolf’s biography and art can provide clinicians with important insights about the course of bipolar disorder.

Identification and individualized prediction of clinical phenotypes in bipolar disorders using neurocognitive data, neuroimaging scans and machine learning.

Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may not effectively capture underlying pathophysiological mechanisms of dysfunction. Here, we report a novel approach of identifying and validating distinct and biologically meaningful clinical phenotypes of bipolar disorders using both unsupervised and supervised machine learning techniques. First, neurocognitive data were analyzed using an unsupervised machine learning approach and two distinct clinical phenotypes identified namely; phenotype I and phenotype II. Second, diffusion weighted imaging scans were pre-processed using the tract-based spatial statistics (TBSS) method and 'skeletonized' white matter fractional anisotropy (FA) and mean diffusivity (MD) maps extracted. The 'skeletonized' white matter FA and MD maps were entered into the Elastic Net machine learning algorithm to distinguish individual subjects' phenotypic labels (e.g. phenotype I vs. phenotype II). This calculation was performed to ascertain whether the identified clinical phenotypes were biologically distinct. Original neurocognitive measurements distinguished individual subjects' phenotypic labels with 94% accuracy (sensitivity=92%, specificity=97%). TBSS derived FA and MD measurements predicted individual subjects' phenotypic labels with 76% and 65% accuracy respectively. In addition, individual subjects belonging to phenotypes I and II were distinguished from healthy controls with 57% and 92% accuracy respectively. Neurocognitive task variables identified as most relevant in distinguishing phenotypic labels included; Affective Go/No-Go (AGN), Cambridge Gambling Task (CGT) coupled with inferior fronto-occipital fasciculus and callosal white matter pathways. These results suggest that there may exist two biologically distinct clinical phenotypes in bipolar disorders which can be identified from healthy controls with high accuracy and at an individual subject level. We suggest a strong clinical utility of the proposed approach in defining and validating biologically meaningful and less heterogeneous clinical sub-phenotypes of major psychiatric disorders.

Virginia Woolf, neuroprogression, and bipolar disorder.

Family history and traumatic experiences are factors linked to bipolar disorder. It is known that the lifetime risk of bipolar disorder in relatives of a bipolar proband are 5-10% for first degree relatives and 40-70% for monozygotic co-twins. It is also known that patients with early childhood trauma present earlier onset of bipolar disorder, increased number of manic episodes, and more suicide attempts. We have recently reported that childhood trauma partly mediates the effect of family history on bipolar disorder diagnosis. In light of these findings from the scientific literature, we reviewed the work of British writer Virginia Woolf, who allegedly suffered from bipolar disorder. Her disorder was strongly related to her family background. Moreover, Virginia Woolf was sexually molested by her half siblings for nine years. Her bipolar disorder symptoms presented a pernicious course, associated with hospitalizations, suicidal behavioral, and functional impairment. The concept of neuroprogression has been used to explain the clinical deterioration that takes places in a subgroup of bipolar disorder patients. The examination of Virgina Woolf's biography and art can provide clinicians with important insights about the course of bipolar disorder.

Cognitive performance and psychosocial functioning in patients with bipolar disorder, unaffected siblings, and healthy controls

Objective: To assess cognitive performance and psychosocial functioning in patients with bipolar disorder (BD), in unaffected siblings, and in healthy controls. Methods: Subjects were patients with BD (n=36), unaffected siblings (n=35), and healthy controls (n=44). Psychosocial functioning was accessed using the Functioning Assessment Short Test (FAST). A sub-group of patients with BD (n=21), unaffected siblings (n=14), and healthy controls (n=22) also underwent a battery of neuropsychological tests: California Verbal Learning Test (CVLT), Stroop Color and Word Test, and Wisconsin Card Sorting Test (WCST). Clinical and sociodemographic characteristics were analyzed using one-way analysis of variance or the chi-square test; multivariate analysis of covariance was used to examine differences in neuropsychological variables. Results: Patients with BD showed higher FAST total scores (23.90±11.35) than healthy controls (5.86±5.47; p < 0.001) and siblings (12.60±11.83; p 0.001). Siblings and healthy controls also showed statistically significant differences in FAST total scores (p = 0.008). Patients performed worse than healthy controls on all CVLT sub-tests (p < 0.030) and in the number of correctly completed categories on WCST (p = 0.030). Siblings did not differ from healthy controls in cognitive tests. Conclusion: Unaffected siblings of patients with BD may show poorer functional performance compared to healthy controls. FAST scores may contribute to the development of markers of vulnerability and endophenotypic traits in at-risk populations.

Increased BDNF levels after electroconvulsive therapy in patients with major depressive disorder: A meta-analysis study.

OBJECTIVE: We performed a systematic review and meta-analysis to estimate brain-derived neurotrophic factor (BDNF) level in patients with major depressive disorder (MDD) after electroconvulsive therapy (ECT). METHOD: A comprehensive search of the Cochrane Library, MEDLINE, LILACS, Grey literature, and EMBASE was performed for papers published from January 1990 to April 2016. The following key terms were searched: "major depressive disorder", "unipolar depression", "brain-derived neurotrophic factor", and "electroconvulsive therapy". RESULTS: A total of 252 citations were identified by the search strategy, and nine studies met the inclusion criteria of the meta-analysis. BDNF levels were increased among patients with MDD after ECT (P value = 0.006). The standardized mean difference was 0.56 (95% CI: 0.17-0.96). Additionally, we found significant heterogeneity between studies (I2 = 73%). CONCLUSION: Our findings suggest a potential role of BDNF as a marker of treatment response after ECT in patients with MDD.