AIMC Topic: Bipolar Disorder
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Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.
Acta psychiatrica Scandinavica
Aug 6, 2018
OBJECTIVE: This study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses.
Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder.
Progress in neuro-psychopharmacology & biological psychiatry
Jun 26, 2018
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...
Machine learning improved classification of psychoses using clinical and biological stratification: Update from the bipolar-schizophrenia network for intermediate phenotypes (B-SNIP).
Schizophrenia research
May 26, 2018
Volumetric brain magnetic resonance imaging predicts functioning in bipolar disorder: A machine learning approach.
Journal of psychiatric research
May 26, 2018
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
May 15, 2018
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...
A morphometric signature of depressive symptoms in unmedicated patients with mood disorders.
Acta psychiatrica Scandinavica
Apr 22, 2018
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...
Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.
F1000Research
Apr 18, 2018
We sought to test the hypothesis that transcriptome-level gene signatures are differentially expressed between male and female bipolar patients, prior to lithium treatment, in a patient cohort who later were clinically classified as lithium treatmen...
A YinYang bipolar fuzzy cognitive TOPSIS method to bipolar disorder diagnosis.
Computer methods and programs in biomedicine
Feb 6, 2018
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...
The impact of machine learning techniques in the study of bipolar disorder: A systematic review.
Neuroscience and biobehavioral reviews
Jul 18, 2017
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....
Using machine learning and surface reconstruction to accurately differentiate different trajectories of mood and energy dysregulation in youth.
PloS one
Jul 6, 2017
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...
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