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相关概念视频

Long-term Depression01:05

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.
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Depression: Overview01:18

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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

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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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大脑形态特征可以预测晚年抑郁症的抑郁症状表型,使用深度学习模型.

Bing Cao1, Erkun Yang2, Lihong Wang3

  • 1College of Intelligence and Computing, Tianjin University, Tianjin, China.

Frontiers in neuroscience
|August 4, 2023
PubMed
概括

深度学习模型确定了特定的大脑区域,包括前扣带和轨道前皮层,与晚年抑郁症 (LLD) 症状表型相关. 这项研究有助于开发针对LLD的有针对性的治疗方法.

关键词:
阿尔茨海默病的疾病阿尔茨海默病的疾病.认知障碍是一种认知障碍.截面的晚年抑郁症.深度学习是一种深度学习.分数因子评分预测 预测结构性MRI边界

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科学领域:

  • 神经科学是一个神经科学.
  • 精神病学是一个精神病学.
  • 人工智能的人工智能

背景情况:

  • 晚年抑郁症 (LLD) 呈现出复杂的症状概况.
  • 确定LLD症状的特定神经生物学基础对于有效治疗至关重要.

研究的目的:

  • 采用深度学习模型,确定与不同LLD症状表型相关的大脑区域.
  • 研究结构磁共振成像 (sMRI) 在预测这些症状表型方面的实用性.

主要方法:

  • 利用了来自116名LLD患者的sMRI数据的深度学习.
  • 使用3DsMRI贴片预测了五种抑郁症症状因素 (无忧症,自杀倾向,食欲,睡眠障碍,焦虑).
  • 使用ROI级预测准确度识别了歧视性大脑区域.

主要成果:

  • 深度学习模型成功预测了焦虑和自杀因素.
  • 前带带状和轨道前皮质被确定为所有五种症状表型的关键区分区域.
  • 大脑中的局部形态差异与LLD症状表型有关.

结论:

  • 在sMRI上的深度学习对于预测LLD症状表型是有效的.
  • 这些发现凸显了在LLD中针对症状治疗的潜力.
  • 未来的研究应该整合多式联运数据,以提高预测.