香港六合彩中特网

Researchers at聽Max Planck 香港六合彩中特网 Centre for Computational Psychiatry and Ageing Research, and聽Wellcome Trust Centre for Neuroimaging, 香港六合彩中特网 Institute of Neurology聽have developed a computational model describing how someone鈥檚 current mood biases how they react to good and bad events, which they say offers a new explanation for bipolar disorder.

The studywas recently published in聽JAMA Psychiatry

The researchers describe a new understanding of the two-way relationship between our mood and how we respond to rewards. They propose that our mood affects how we perceive the events in our lives. A modest impact of mood on how we see things could be beneficial because it could help individuals quickly adapt to changing environments. However, people whose moods bias their perceptions too strongly may be prone to greater mood swings in reaction to good or bad events, potentially resulting in extreme behaviours.

Computational simulations showed that this 鈥渕ood bias鈥� can contribute to manic, depressive and mixed-affective episodes that are typical of bipolar disorder. The strongest mood biases resulted in more severe episodes and less time in remission.

Lead author Dr Liam Mason says that we currently have a poor understanding of the neurobiological mechanisms that drive bipolar disorder, and explanations of mixed mood states and mood instability are particularly lacking, so his study could bring researchers closer to pinpointing how the disorder works.

In addition to computational tests, the researchers also tested the model in healthy participants in a recent study finding that the extent to which someone鈥檚 mood biases their perceptions predicts how susceptible they are to mood instability in daily life.

The researchers will soon be launching a study involving people with bipolar disorder to test how well their computational simulations hold up in a clinical population.

Further information

• Mason, Eldar and Rutledge. .聽JAMA Psychiatry.聽Published online October 11, 2017. doi:10.1001/jamapsychiatry.2017.3163