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Long-term Depression01:03

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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
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Depressive disorders result from a complex interplay of biological, psychological, and sociocultural factors, each contributing uniquely to the development and persistence of the condition. Understanding these factors provides critical insight into the multifaceted nature of depression.
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Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...
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Related Experiment Video

Updated: Dec 13, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Depression Risk Prediction for Chinese Microblogs via Deep-Learning Methods: Content Analysis.

Xiaofeng Wang1, Shuai Chen2, Tao Li2

  • 1School of Communication, Shenzhen University, Shenzhen, China.

JMIR Medical Informatics
|July 30, 2020
PubMed
Summary

Deep learning models effectively predict depression risk from Chinese microblogs. These advanced methods show promise for identifying individuals with depression and monitoring their mental health conditions.

Keywords:
Chinese microblogsdeep learningdepression risk predictionpretrained language model

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Area of Science:

  • Computational linguistics
  • Mental health informatics
  • Machine learning for healthcare

Background:

  • Depression diagnosis relies on self-reporting, which is often hindered by stigma and time constraints.
  • Automatic depression detection from social media offers a promising alternative to traditional methods.
  • Chinese microblogs provide a rich data source for understanding and predicting mental health conditions.

Purpose of the Study:

  • To investigate the efficacy of state-of-the-art deep learning models for depression risk prediction.
  • To evaluate deep learning performance on a manually annotated Chinese microblog dataset.
  • To compare different deep learning architectures and pretraining strategies for depression detection.

Main Methods:

  • Utilized deep learning models including Bidirectional Encoder Representations from Transformers (BERT), RoBERTa, and XLNET.
  • Assessed depression risk across four levels (0-3) using data from the Chinese microblog Weibo.
  • Compared publicly available models with models further pretrained on a large-scale Weibo dataset.

Main Results:

  • BERT achieved the highest microaveraged F1 score (0.856) for depression risk prediction.
  • RoBERTa set a new benchmark with a macroaveraged F1 score of 0.424 for depression risk levels 1-3.
  • Further pretraining of language representation models enhanced prediction performance compared to publicly released models.

Conclusions:

  • Deep learning models, particularly BERT and RoBERTa, demonstrate superior performance in predicting depression risk from Chinese microblogs.
  • These methods offer a scalable and effective approach for early detection and monitoring of depression.
  • The study highlights the potential of social media data and advanced AI for mental health research and clinical applications.