Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

50
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...
50
Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

80
Schizophrenia, a complex psychiatric disorder, has been historically misunderstood. Early psychological theories attributed its origins to childhood trauma and unresponsive parenting. However, contemporary research largely rejects these notions, favoring the vulnerability-stress hypothesis. This model proposes that individuals with a genetic predisposition to schizophrenia may develop the disorder following exposure to significant environmental stressors. Notably, studies on high-risk...
80
Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders01:27

Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders

513
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...
513
Schizophrenia01:17

Schizophrenia

72
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...
72

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Revealing Structural Brain-Cognition Relationships in Children: A Comparison of Morphometric Similarity and INverse Divergence Networks.

Neuroinformatics·2026
Same author

Early prediction of dementia using fMRI data with a graph convolutional network approach.

Journal of neural engineering·2024
Same author

Special Issue "Machine Learning Methods for Biomedical Data Analysis".

Sensors (Basel, Switzerland)·2023
Same author

Multi-Class Classification of Upper Limb Movements With Filter Bank Task-Related Component Analysis.

IEEE journal of biomedical and health informatics·2023
Same author

Improving pre-movement pattern detection with filter bank selection.

Journal of neural engineering·2022
Same author

Graph Empirical Mode Decomposition-Based Data Augmentation Applied to Gifted Children MRI Analysis.

Frontiers in neuroscience·2022
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 12, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K

优化图形神经网络架构用于使用进化算法的精神分裂症谱系障碍预测.

Shurun Wang1, Hao Tang2, Ryutaro Himeno3

  • 1School of Information Science and Technology, University of Science and Technology of China, Hefei, 230027, China; School of Electrical Engineering and Automation, Hefei University of Technology, Hefei, 230009, China; Graduate School of Medicine, Juntendo University, Tokyo, 1138421, Japan.

Computer methods and programs in biomedicine
|September 18, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种基于进化算法的图形神经网络,用于诊断精神分裂症谱系障碍,其性能高于传统方法,并提供可解释的AI洞察力,以改善患者护理.

关键词:
大脑的功能连接性大脑功能连接性进化算法是一种进化算法.图形神经架构搜索搜索 图形神经架构搜索图表神经网络的神经网络精神分裂症谱系障碍 精神分裂症谱系障碍

更多相关视频

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

981
A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior
06:59

A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

Published on: May 21, 2020

4.0K

相关实验视频

Last Updated: Jun 12, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

981
A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior
06:59

A Pipeline using Bilateral In Utero Electroporation to Interrogate Genetic Influences on Rodent Behavior

Published on: May 21, 2020

4.0K

科学领域:

  • 神经科学是一个神经科学.
  • 人工智能的人工智能
  • 医疗成像医学成像

背景情况:

  • 准确诊断精神分裂症谱系障碍对于患者的治疗结果至关重要.
  • 使用fMRI的功能连接分析为诊断提供了潜在的生物标志物.
  • 现有的方法可能无法完全捕捉大脑数据中的复杂空间关系.

研究的目的:

  • 开发一种用于诊断精神分裂症谱系障碍的先进方法.
  • 提高诊断模型的准确性和可解释性.
  • 利用图形神经网络进行增强的大脑连接分析.

主要方法:

  • 提出了一个基于进化算法的图形神经架构搜索 (EA-GNAS).
  • 使用GNNExplainer来解释模型的解释性.
  • 将该方法应用于精神分裂症谱系障碍患者的多站点数据集.

主要成果:

  • 与传统和深度学习方法相比,EA-GNAS模型实现了更高的性能.
  • 在交叉验证中实现了高精度 (0.8246),F1得分 (0.8438),和AUC (0.8258).
  • 证明了模型能够提供准确和可理解的预测的能力.

结论:

  • 开发的图形神经网络模型增强了精神分裂症谱系障碍的诊断.
  • 这些发现有助于进一步了解精神分裂症谱系障碍中大脑功能的理解.
  • 该方法显示了作为诊断精神分裂症谱系障碍的新生物标志物的潜力.