AIMC Topic: Bipolar Disorder
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Longitudinal brain age in first-episode mania youth treated with lithium or quetiapine.
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Apr 13, 2025
It is unclear if lithium and quetiapine have neuroprotective effects, especially in early stages of bipolar and schizoaffective disorders. Here, an age-related multivariate brain structural measure (i.e., brain-PAD) at baseline and changes in respons...
Machine learning-based differentiation of schizophrenia and bipolar disorder using multiscale fuzzy entropy and relative power from resting-state EEG.
Translational psychiatry
Apr 12, 2025
Schizophrenia (SZ) and bipolar disorder (BD) pose diagnostic challenges due to overlapping clinical symptoms and genetic factors, often resulting in misdiagnosis and suboptimal treatment outcomes. This study aimed to identify EEG-based biomarkers tha...
Altered resting-state brain activity in patients with major depression disorder and bipolar disorder: A regional homogeneity analysis.
Journal of affective disorders
Mar 11, 2025
BACKGROUND: Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but...
Using Natural Language Processing Methods to Build the Hypersexuality in Bipolar Reddit Corpus: Infodemiology Study of Reddit.
JMIR infodemiology
Mar 6, 2025
BACKGROUND: Bipolar is a severe mental health condition affecting at least 2% of the global population, with clinical observations suggesting that individuals experiencing elevated mood states, such as mania or hypomania, may have an increased propen...
Pharmacogenomics and response to lithium in bipolar disorder.
Pharmacogenomics
Feb 25, 2025
AIMS: The present review explores the existing evidence on pharmacogenomic tests for prediction of lithium response in the treatment of bipolar disorder. We focused our research article on reports describing findings from genome-wide association stud...
Prediction of early-onset bipolar using electronic health records.
Journal of child psychology and psychiatry, and allied disciplines
Feb 18, 2025
BACKGROUND: Early identification of bipolar disorder (BD) provides an important opportunity for timely intervention. In this study, we aimed to develop machine learning models using large-scale electronic health record (EHR) data including clinical n...
Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.
Neuroradiology
Jan 18, 2025
INTRODUCTION: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI a...
Immune-based Machine learning Prediction of Diagnosis and Illness State in schizophrenia and bipolar Disorder: How data bias and overfitting were avoided.
Brain, behavior, and immunity
Dec 17, 2024
In a letter critiquing our manuscript, Takefuji highlights general pitfalls in machine learning, without directly engaging with our study. The comments provide generic advice rather than a specific critique of our methods or findings. Despite raising...
RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders.
International journal of molecular sciences
Dec 3, 2024
Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, schizophrenia, and schizoaffective disorder are particularly difficult to distinguish, as they share...
Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations.
Brain, behavior, and immunity
Nov 29, 2024
Skorobogatov et al. developed supervised machine learning models to predict diagnoses and illness states in schizophrenia and bipolar disorder. However, their reliance on bootstrap forests and generalized regressions introduces significant biases in ...
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