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.Peer reviewe

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.Peer reviewe

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

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

Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study

Abstract Bipolar disorder (BD) is a common disorder with high reoccurrence rate in general population. It is critical to have objective biomarkers to identify BD patients at an individual level. Neurocognitive signatures including affective Go/No-go task and Cambridge Gambling task showed the potential to distinguish BD patients from health controls as well as identify individual siblings of BD patients. Moreover, these neurocognitive signatures showed the ability to be replicated at two independent cohorts which indicates the possibility for generalization. Future studies will examine the possibility of combining neurocognitive data with other biological data to develop more accurate signatures

Erratum to: Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study

In the original version of this article (Wu et al. 2017), published on 1 September 2017, the name of author ‘Bo Cao’ was wrongly displayed. In this Erratum the incorrect name and correct name are shown. The original publication of this article has been corrected