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相关概念视频

Long-term Depression01:03

Long-term Depression

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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.
Calcium Ion Concentration Mechanism
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Depression: Overview01:18

Depression: Overview

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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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Depressive Disorders: Etiology01:27

Depressive Disorders: Etiology

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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.
Biological Factors in Depression
Biological predispositions significantly influence the risk of developing depressive disorders. Genetic studies highlight the role of variations in the serotonin transporter...
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Depressive Disorders: MDD and Dysthymia01:27

Depressive Disorders: MDD and Dysthymia

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Depressive disorders are a group of mental health conditions characterized by pervasive feelings of sadness, diminished pleasure in life, and a significant impact on daily functioning. These conditions are most prevalent in individuals during their 30s and affect women at twice the rate of men. Contrary to popular belief, younger individuals are generally more susceptible to these disorders than older adults. Two key types of depressive disorders include Major Depressive Disorder (MDD) and...
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Author Spotlight: Therapeutic Benefit of Closed-Loop Deep Brain Stimulation in Depression Treatment
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抑郁症MIGNN:一个基于多实例学习的抑郁症检测模型与图形神经网络.

Shiwen Zhao1,2,3, Yunze Zhang1,2,3, Yikai Su1,3

  • 1HACI Laboratory, Sydney Smart Technology College, Northeastern University, Shenyang 110167, China.

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概括
此摘要是机器生成的。

这项研究引入了使用多式联络传感器数据检测抑郁症的新框架. 该系统分析时间行为模式,以更准确,更长期地评估疾病.

关键词:
抑郁症的识别 抑郁症的识别图形神经网络是一个神经网络.多维边缘的多维边缘多式联络 多式联络 多式联络

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科学领域:

  • 计算精神病学是一种计算精神病学.
  • 情感计算是一种情感计算.
  • 机器学习用于医疗保健

背景情况:

  • 全球抑郁症患病率凸显了在诊断方面需要技术解决方案的需要.
  • 基于传感器的系统可以增加有限的资源,用于早期检测抑郁症.
  • 现有的方法往往侧重于瞬间的表达,错过了慢性疾病模式.

研究的目的:

  • 通过多式联络数据 (视频,音频,文本) 提出一种新的抑郁症检测框架.
  • 通过分析时间行为模式来解决抑郁症作为一种慢性疾病.
  • 通过考虑长期指标,提高抑郁症检测的准确性.

主要方法:

  • 使用了多式联络数据 (视频,音频,转录文本) 的基准数据集.
  • 开发了一个框架,将视频细分为发言级实例,使用Gated Recurrent Units (GRU) 来进行上下文表示.
  • 构造的图形以语句嵌入为节点,通过双重关系 (时间和相关信息) 连接.
  • 使用图形神经网络 (GNN) 来学习多维边缘关系,并在时间依赖关系中对齐多式表示.

主要成果:

  • 在AVEC2014上取得了卓越的性能,平均绝对误差 (MAE) 为5.25,根平均平方误差 (RMSE) 为6.75.
  • 在AVEC2019上表现出强的结果,一致性相关系数 (CCC) 为0.554和RMSE为4.61.
  • 展示了对现有方法的显著改进,专注于瞬间表达.

结论:

  • 拟议的框架通过分析来自多式联络数据的时间行为模式,有效地检测抑郁症.
  • 集成GRU和GNN的基于图形的方法捕捉了复杂的时间依赖性,以提高诊断准确性.
  • 这种方法在利用技术来长期评估抑郁症方面取得了重大进展.