Interoceptive sensitivity and anxiety disorders / 中华行为医学与脑科学杂志

Anxiety disorders are the most prevalent mental health condition.More and more studies have found that interoceptive sensitivity, such as the sensitivity of the body to a series of physiological activities such as heart rate, respiration, blood pressure and so on, is closely related to the susceptibility to anxiety disorders.So, understanding the role of interoception in the occurrence of anxiety disorders plays an important role in understanding the pathogenesis of the disease and guiding treatment.This article reviewed the pathogenesis of anxiety disorders, the interaction between increased interoceptive sensitivity and abnormal activation of the amygdala, which results in pathological anxiety, the insula also regulates emotional responses by regulating interoception.It is also associated with genes and neurotransmitters, which may be important biomarkers of anxiety disorders.At the same time, interoception is also associated with genes and neurotransmitters, which may also be important biomarkers of anxiety disorders.In terms of treatment, we can use respiratory therapy to regulate respiratory perception, apply rTMS to stimulate the relevant network of interoception, and use electroencephalography to reflect interoception biofeedback and other treatment methods to improve interoception, to alleviate anxiety symptoms.In conclusion, the abnormal sensitivity of interoception plays an important role in the occurrence of anxiety disorders.Currently, there are many therapeutic methods for the treatment of anxiety disorders based on interoception, but the relevant therapeutic mechanism is unclear.Therefore, future research needs to explore the mechanism of interoception in anxiety and explore the mechanism of related treatment.

Deep learning in predicting the treatment outcomes of depressed patients / 中华行为医学与脑科学杂志

The optimal antidepressant therapies for different patients have been identified mostly by trial and error. Selecting an effective treatment based on the specific biomarkers may be an important step toward personalized treatment of depression. Deep learning is a branch of machine learning, that is capable of processing high-dimensional and complex data.It automatically extracts and learns from large amounts of demographic, clinical symptoms, genomics and neuroimaging data. Researchers have been using deep learning algorithms to develop prediction model of anti-depressant response in order to guide clinicians to make a precise prescription for depression and further advance personalized treatment globally. This article reviews the application of deep learning in predicting the treatment outcomes of depression. Additionally, deep learning based on multi-omics data applied in treatment outcome's prediction is direction with prospects in the future.