AIMC Topic: Bipolar Disorder

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Automated Speech Analysis for Screening and Monitoring Bipolar Depression: Machine Learning Model Development and Interpretation Study.

JMIR medical informatics
BACKGROUND: Depressive episodes in bipolar disorder are frequent, prolonged, and contribute substantially to functional impairment and reduced quality of life. Therefore, early and objective detection of bipolar depression is critical for timely inte...

Mapping neurophysiological and molecular profiles of heterogeneity and homogeneity in schizophrenia-bipolar disorder.

Science advances
The heterogeneity of psychotic disorders leads to instability in subjectively defined diagnoses. This study used a machine learning framework termed common orthogonal basis extraction (COBE) to decompose electroencephalography-based functional connec...

The Genetic Architecture of the Human Corpus Callosum and its Subregions.

Nature communications
The corpus callosum (CC) is the largest set of white matter fibers connecting the two hemispheres of the brain. In humans, it is essential for coordinating sensorimotor responses and performing associative or executive functions. Identifying which ge...

The application of amplitude of low-frequency fluctuations metrics in the diagnosis and prediction of treatment response as well as their associated genes and biological processes in patients with bipolar disorder.

Translational psychiatry
While previous studies have reported functional abnormalities in the prefrontal-limbic-subcortical circuit, the treatment effects on this activity remain unclear. This longitudinal study aimed to investigate spontaneous brain activity in bipolar diso...

Natural lithium isotope variations in serum after lithium administration as a novel biomarker for differentiating schizophrenia and bipolar disorder.

Translational psychiatry
Accurate differentiation of schizophrenia (SZ) and bipolar disorder (BD) is crucial for effective clinical management. However, current diagnostic methods, which rely heavily on subjective assessments, are prone to high rates of misdiagnosis. This st...

Using Wearable Device and Machine Learning to Predict Mood Symptoms in Bipolar Disorder: Development and Usability Study.

JMIR medical informatics
BACKGROUND: Bipolar disorder (BD) is a highly recurrent disorder. Early detection, early intervention, and prevention of recurrent bipolar mood symptoms are key to a better prognosis.

Detecting suicide risk in bipolar disorder patients from lymphoblastoid cell lines genetic signatures.

Translational psychiatry
This research aimed to develop a machine learning algorithm to predict suicide risk in bipolar disorder (BD) patients using RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) between high...

Identifying key brain pathology in bipolar and unipolar depression using a region-specific brain aging trajectories approach: Insights from the Taiwan Aging and Mental Illness Cohort.

Psychological medicine
BACKGROUND: Identifying key areas of brain dysfunction in mental illness is critical for developing precision diagnosis and treatment. This study aimed to develop region-specific brain aging trajectory prediction models using multimodal magnetic reso...

An open dataset and machine learning algorithms for Niacin Skin-Flushing Response based screening of psychiatric disorders.

BMC psychiatry
BACKGROUND: Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approac...