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

Abstract

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.

Neurobiology of bipolar disorders: a review of genetic components, signaling pathways, biochemical changes, and neuroimaging findings

“…241 More recently, a Big Data Task Force from the International Society for Bipolar Disorders has expanded this literature review and discussed issues to be addressed in machine learning-based studies, including the main barriers for applying these techniques and strategies to approach them. 242 Since human behavior, including cognition, emotion, and social interaction, reflects complex neural circuit communication, 243 the signs and symptoms we observe in individuals with psychiatric disorders could be understood as the manifestation of different brain circuitry dysfunction. 244 Diffusion tensor imaging (DTI) is a neuroimaging technique used to evaluate white matter fiber tract connectivity between different regions, both proximal and distal.…”

“… 241 More recently, a Big Data Task Force from the International Society for Bipolar Disorders has expanded this literature review and discussed issues to be addressed in machine learning-based studies, including the main barriers for applying these techniques and strategies to approach them. 242 …”

Will machine learning applied to neuroimaging in bipolar disorder help the clinician? A critical review and methodological suggestions

“…Therefore, more and more MRI studies have attempted to use ML to distinguish BD from HC and other psychiatric disorders based on brain images. Previous work reviewed existing studies in this area but without highlighting the biases that could have compromised the reliability of some results 17,18 . In this literature review, we will first describe ML general principles, provide some advices for proper applications of ML techniques, and show its potential for neuroimaging research of BD.…”

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

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