Neuroprogression and illness trajectories in bipolar disorder

Kapczinski, Natalia Soncini; Mwangi, Benson; Cassidy, Ryan M.; Librenza-Garcia, Diego; Bermudez, Mariane Bagatin; Kauer-Sant’Anna, Márcia; Kapczinski, Flávio; Passos, Ives Cavalcante

doi:10.1080/14737175.2017.1240615

Cited by 105 publications

(51 citation statements)

References 73 publications

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“…Therefore, population and largely representative studies, including other psychiatric or neuropsychiatric disorders, should be conducted to ensure generalization of the proposed models. Third, we still do not know how these models will perform in face of patients from different stages of the disorder . In this sense, a recent study showed that a machine learning model developed by using the relevance vector machine algorithm and white matter from structural MRI was more accurate in identifying patients with BD at the late stage …”

Section: How Will Machine Learning and Big Data Analytics Contribute mentioning

confidence: 99%

“…Third, we still do not know how these models will perform in face of patients from different stages of the disorder. 8,15,61 In this sense, a recent study showed that a machine learning model developed by using the relevance vector machine algorithm and white matter from structural MRI was more accurate in identifying patients with BD at the late stage. 62…”

Section: Diagnostic Studiesmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: How Will Machine Learning and Big Data Analytics Contribute mentioning

confidence: 99%

Section: Diagnostic Studiesmentioning

confidence: 99%

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

et al. 2019

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“…2 Individuals with BD can experience a progressive illness course with shortening of interepisode intervals and impaired treatment response. 4 Recent neuroimaging studies found thinner prefrontal and temporal cortices in BD type I and II. 4 Recent neuroimaging studies found thinner prefrontal and temporal cortices in BD type I and II.…”

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confidence: 99%

“…3 Although the neural underpinnings of illness progression remain poorly understood, it has been suggested that mood episodes can cause lasting neurobiological alterations. 4 Recent neuroimaging studies found thinner prefrontal and temporal cortices in BD type I and II. 5,6 These cortical regions are believed to play central roles in processing and regulation of emotions.…”

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confidence: 99%

Mood episodes are associated with increased cortical thinning: A longitudinal study of bipolar disorder type II

et al. 2019

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| INTRODUC TI ONBipolar disorder (BD) is a chronic psychiatric illness characterized by episodic disturbances in mood and activity levels and affects 2%-3% of the population worldwide. 1 BD type I is defined by at least one manic episode, while BD type II is characterized by alternating episodes of hypomania and depression. The clinical significance of both subtypes is comparable in terms of illness burden, role impairments, AbstractObjectives: Previous studies found evidence for thinner frontotemporal cortices in bipolar disorder (BD), yet whether this represents a stable disease trait or an effect of mood episodes remains unknown. Here, we assessed the reproducibility of thinner frontotemporal cortices in BD type II, compared longitudinal changes in cortical thickness between individuals with BD type II and healthy controls (HCs), and examined the effect of mood episodes on cortical thickness change.Methods: Thirty-three HCs and 29 individuals with BD type II underwent 3T magnetic resonance imaging at baseline, as published previously, and 2.4 years later, at follow-up. Cross-sectional and longitudinal analyses of cortical thickness were performed using Freesurfer, and relationships with mood episodes from baseline to follow-up were assessed.Results: Individuals with BD type II had thinner left and right prefrontal and left temporal cortex clusters at follow-up (all corrected P < 0.001), consistent with baseline results. Both groups showed widespread longitudinal cortical thinning, and patients had increased thinning in a left temporal cortex cluster compared to HCs (corrected P < 0.001). Patients with more (>2) depressive episodes between baseline and followup had greater left temporal cortical thinning than patients with fewer depressive episodes (corrected P < 0.05). In addition, patients with more depressive episodes had greater thinning in bilateral ventromedial prefrontal clusters relative to HCs (uncorrected P < 0.05), yet these results did not survive correction for multiple comparisons. Conclusions:Together, these findings support reduced frontotemporal cortical thickness in BD type II and provide the first preliminary evidence for an association between depressive episodes and increased cortical thinning. K E Y W O R D Sbipolar disorder, cortical thickness, cortical thinning, depressive episodes, longitudinal study, progressive changes

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“…Another key thrust of this notion is that the interplay between these neuroplasticity substrates is important for appropriate network function, not isolated factors per se. Studies of these interrelated processes are imperative given evidence that interventions applied earlier in the course of disease are more likely to achieve disease modification, whereas those applied later may have a significant but more limited effect [446, 447]. …”

Section: Implications For Translation Unresolved Issues and Futmentioning

confidence: 99%

Physical Activity Modulates Common Neuroplasticity Substrates in Major Depressive and Bipolar Disorder

Phillips¹

2017

Neural Plasticity

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Mood disorders (MDs) are chronic, recurrent mental diseases that affect millions of individuals worldwide. Although the biogenic amine model has provided some clinical utility, a need remains to better understand the interrelated mechanisms that contribute to neuroplasticity deficits in MDs and the means by which various therapeutics mitigate them. Of those therapeutics being investigated, physical activity (PA) has shown clear and consistent promise. Accordingly, the aims of this review are to (1) explicate key modulators, processes, and interactions that impinge upon multiple susceptibility points to effectuate neuroplasticity deficits in MDs; (2) explore the putative mechanisms by which PA mitigates these features; (3) review protocols used to induce the positive effects of PA in MDs; and (4) highlight implications for clinicians and researchers.

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Neuroprogression and illness trajectories in bipolar disorder

Cited by 105 publications

References 73 publications

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

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

Mood episodes are associated with increased cortical thinning: A longitudinal study of bipolar disorder type II

Physical Activity Modulates Common Neuroplasticity Substrates in Major Depressive and Bipolar Disorder

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