Biomarkers, Clinical Characteristics and Treatment of Bipolar Disorder

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 1244

Special Issue Editors


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Guest Editor
Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55905, USA
Interests: bipolar depression; augmentation strategies; pharmacogenomics; biomarkers

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Guest Editor
1. Department of Psychiatry & Psychology, Mayo Clinic, Rochester, MN 55905, USA
2. Department of Neuroscience, Padova Neuroscience Center, University of Padova, 35127 Padua, Italy
Interests: psychopathology; bipolar disorder; clinical assessment; mental illness; neuropsychopharmacology

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Guest Editor
Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
Interests: bipolar disorder; major depression

Special Issue Information

Dear Colleagues,

This Special Issue will focus on recent advances in the diagnosis and pharmacological treatment of bipolar disorder (BD). BD is a complex and chronic condition with high clinical heterogeneity, frequent psychiatric and general medicine comorbidities, and variable course trajectories. Depression usually represents the prominent mood polarity over the lifespan of people with BD, accounting for its significant morbidity and mortality. Depression may also account for the delayed recognition of BD until a full-threshold (hypo-) manic episode occurs, potentially leading to improper clinical management, increased risk for mixed features of BD, rapid-cycling course, and suicidal behavior, among other outcomes. Pharmacotherapy options both for the acute and maintenance phase of BD have included several medicines, namely, mood stabilizers, antipsychotics, and antidepressants, requiring polypharmacotherapy in most cases to achieve stabilization although there has been concern regarding the overall efficacy as well as the emergence of side effects (eg., metabolic syndrome and antidepressant treatment emergent mania). In recent decades, intense efforts from pre-clinical and clinical perspectives have been dedicated towards developing a better understanding of the neurobiological (i.e., neuro imaging and peripheral and genetic biomarkers) underpinnings of bipolar depression. However, additional insights are warranted, especially compared to acute mania, for which lithium was proposed as an avenue toward understanding the neurobiological bases (though no equivalent agent exists for bipolar depression, to date). Therefore, greater precision on pharmacotherapy recommendations personalizing treatment may potentially improve overall effectiveness. Similarly, psychosocial interventions (psychoeducation, interpersonal, or cognitive behavioral therapy) and neuromodulation techniques can improve overall outcomes.

In this Special Issue, we will welcome case reports, qualitative and quantitative reviews, and clinical studies underscoring diagnostic controversies, clinical correlations with treatment outcomes, risk and protective factors, perspectives, and challenges in new pharmacological treatments as well as psychosocial and neuromodulation interventions that may all provide a better understanding of the pathophysiology and effective ways to personalize treatment in BD.

Dr. Nicolas A. Nunez
Prof. Dr. Michele Fornaro
Dr. Alessandro Miola
Dr. Boney Joseph
Guest Editors

Manuscript Submission Information

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Keywords

  • bipolar disorder
  • pharmacological treatment
  • biomarkers

Published Papers (1 paper)

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Research

16 pages, 3052 KiB  
Article
Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models
by Yang Huang, Jingbo Zhang, Kewei He, Xue Mo, Renqiang Yu, Jing Min, Tong Zhu, Yunfeng Ma, Xiangqian He, Fajin Lv, Du Lei and Mengqi Liu
Diagnostics 2024, 14(4), 389; https://doi.org/10.3390/diagnostics14040389 - 10 Feb 2024
Viewed by 706
Abstract
Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution [...] Read more.
Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders. Full article
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