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相关概念视频

Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

103
Schizophrenia, a severe psychiatric disorder, arises from a complex interplay of biological factors, including genetic predisposition, structural brain abnormalities, neurotransmitter dysregulation, and developmental irregularities. These factors collectively contribute to the onset and progression of the disorder, which typically manifests in late adolescence or early adulthood.
Genetic Factors in Schizophrenia
The genetic basis of schizophrenia is strongly supported by family and twin...
103
Schizophrenia01:17

Schizophrenia

146
Schizophrenia, a term introduced by Swiss psychiatrist Eugen Bleuler in 1911, describes a severe psychological disorder marked by profound disruptions in attention, thought processes, language, emotion, and interpersonal relationships. The core feature of schizophrenia is psychosis — a state characterized by a fundamental detachment from reality. This disconnection manifests through distorted logic, impaired perception, and atypical behavior, severely affecting the lives of those...
146
Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders01:27

Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders

756
Schizophrenia is a neurodevelopmental disorder whose origins are rooted in complex genetic components. Despite our burgeoning understanding, the pathophysiology of this disorder remains incompletely deciphered.
Researchers have identified genetic factors that increase susceptibility to schizophrenia, underscoring the intricate interplay between genetics and environment in disease development. At the core of schizophrenia's pathophysiology is excessive dopaminergic neurotransmission within...
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相关实验视频

Updated: Jul 24, 2025

Brain Mapping Using a Graphene Electrode Array
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从脑网络特征中自动识别精神分裂症,使用图形卷积神经网络.

Guimei Yin1, Ying Chang2, Yanli Zhao3

  • 1College of Computer Science and Technology, Taiyuan Normal University, City Jinzhong 030619 Shanxi, China.

Asian journal of psychiatry
|July 7, 2023
PubMed
概括

这项研究引入了一种新的图形卷积神经网络 (GCN) 模型,用于使用电脑电图 (EEG) 数据诊断精神分裂症. 该GCN模型实现了90.01%的准确性,识别了用于诊断的重要大脑区域.

关键词:
一个时代的长度.频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频段频频频频频频频频频频频频频频频频频频频频频频频频频频频频频频频频频功能连接度量表功能连接度量表图表卷积神经网络的神经网络.精神分裂症是一种精神分裂症.

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Modeling the Functional Network for Spatial Navigation in the Human Brain
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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相关实验视频

Last Updated: Jul 24, 2025

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

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科学领域:

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

背景情况:

  • 精神分裂症的诊断依赖于主观的临床经验,缺乏客观的方法.
  • 图形卷积神经网络 (GCN) 显示出在精神疾病中分析复杂的空间关联信息的前景.

研究的目的:

  • 开发和验证使用GCN和静止电脑电图 (EEG) 数据的自动精神分裂症识别模型.
  • 确定关键的大脑区域及其网络特征对于精神分裂症诊断至关重要.

主要方法:

  • 利用了来自103名精神分裂症第一发作患者和92名正常对照者的静止状态EEG数据.
  • 使用来自时间域,频域和大脑网络分析的节点特征训练了一个GCN模型.
  • 采用十倍交叉验证来评估模型性能,重点关注theta频段和相锁值.

主要成果:

  • 对于精神分裂症,GCN模型实现了高识别准确率90.01%.
  • 额叶被确定为区分精神分裂症患者与对照者的最重要的区域.
  • 在显著区域和临床指标中探索节点拓特征之间的相关性.

结论:

  • 提出的基于GCN的方法显示出高适用性和准确性,用于对象识别和诊断精神分裂症.
  • 这种方法提供了一个新的,数据驱动的工具来补充精神分裂症的现有诊断实践.