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Bipolar Disorder

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Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.

PloS one
Difficulty regulating positive mood and energy is a feature that cuts across different pediatric psychiatric disorders. Yet, little is known regarding the neural mechanisms underlying different developmental trajectories of positive mood and energy r...

The impact of machine learning techniques in the study of bipolar disorder: A systematic review.

Neuroscience and biobehavioral reviews
Machine learning techniques provide new methods to predict diagnosis and clinical outcomes at an individual level. We aim to review the existing literature on the use of machine learning techniques in the assessment of subjects with bipolar disorder....

A YinYang bipolar fuzzy cognitive TOPSIS method to bipolar disorder diagnosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Bipolar disorder is often mis-diagnosed as unipolar depression in the clinical diagnosis. The main reason is that, different from other diseases, bipolarity is the norm rather than exception in bipolar disorder diagnosis. Yi...

A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.

Acta psychiatrica Scandinavica
OBJECTIVE: A growing literature indicates that unipolar depression and bipolar depression are associated with alterations in grey matter volume. However, it is unclear to what degree these patterns of morphometric change reflect symptom dimensions. H...

Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.

Journal of psychiatric research
Neuroimaging studies have been steadily explored in Bipolar Disorder (BD) in the last decades. Neuroanatomical changes tend to be more pronounced in patients with repeated episodes. Although the role of such changes in cognition and memory is well es...

Emotional hyper-reactivity and cardiometabolic risk in remitted bipolar patients: a machine learning approach.

Acta psychiatrica Scandinavica
OBJECTIVE: Remitted bipolar disorder (BD) patients frequently present with chronic mood instability and emotional hyper-reactivity, associated with poor psychosocial functioning and low-grade inflammation. We investigated emotional hyper-reactivity a...

Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Recent studies demonstrate that functional disruption in resting-state networks contributes to cognitive and affective symptoms of bipolar disorder (BD), however, the functional connectivity (FC) pattern underlying BD II depression within...

Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.

Acta psychiatrica Scandinavica
OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.

Brain Age in Early Stages of Bipolar Disorders or Schizophrenia.

Schizophrenia bulletin
BACKGROUND: The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resona...