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Psychological and Sociocultural Causes of Schizophrenia01:29

Psychological and Sociocultural Causes of Schizophrenia

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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...
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Schizophrenia01:17

Schizophrenia

57
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...
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Biological Causes of Schizophrenia01:29

Biological Causes of Schizophrenia

35
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...
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Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders01:27

Psychosis: Pathophysiology of Schizophrenia and Other Psychotic Disorders

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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...
385
Negative and Cognitive Symptoms of Schizophrenia01:30

Negative and Cognitive Symptoms of Schizophrenia

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Negative symptoms of schizophrenia indicate a reduction or absence of typical behaviors and emotional responses found in healthy individuals, while positive symptoms reflect an excess or distortion of normal functioning.
Negative Symptoms
Negative symptoms of schizophrenia manifest as deficits in normal emotional and behavioral functioning, profoundly impacting daily life. Individuals with schizophrenia often display a flat affect, characterized by a near-total absence of emotional expression,...
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相关实验视频

Updated: May 29, 2025

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
13:08

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

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使用数字表型化数据预测精神分裂症患者的精神状态.

Thierry Jean1,2, Rose Guay Hottin1, Pierre Orban1,2

  • 1Research Center of the Montreal Mental Health University Institute, Montreal, Canada.

PLOS digital health
|February 7, 2025
PubMed
概括

机器学习模型可以使用数字表型化数据预测心理状态. 计算数据不平衡的顺序回归模型,提供与二进制分类相比的性能,用于更丰富,可解释的预测.

科学领域:

  • 数字化表型化是指数字化表型化.
  • 机器学习在心理健康中的应用
  • 精神病学数据分析

背景情况:

  • 机器学习 (ML) 显示出预测精神病患者心理状态的前景.
  • 以前的研究往往忽视了临床评级和数据不平衡的顺序性质.

研究的目的:

  • 用数字表型化数据评估用于预测心理状态的ML算法.
  • 为了比较顺序回归和二进制分类用于心理状态预测.
  • 评估预测地平线和算法选择 (XGBoost与LSTM) 对预测性能的影响.

主要方法:

  • 在从精神分裂症患者的CrossCheck数据集 (6,364次调查,23,551个传感器日) 上训练了120个ML模型.
  • 使用XGBoost和LSTM算法利用顺序回归和二进制分类任务.
  • 在同一天,第二天和下周的预测地平线上评估模型.

主要成果:

  • 大多数模型的表现明显超过了基线,精度在58%-73%之间.
  • 忽视数据不平衡的指标高估了业绩.
  • XGBoost 模型的性能与 LSTM 模型相比或更好.
  • 随着预测时间的延长,业绩略有下降.

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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相关实验视频

Last Updated: May 29, 2025

Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia
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Measurement of Fronto-limbic Activity Using an Emotional Oddball Task in Children with Familial High Risk for Schizophrenia

Published on: December 2, 2015

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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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结论:

  • 使用适当的不平衡感知指标的顺序回归模型提供了临床上有价值和可解释的预测.
  • 这些模型与二进制分类性能相匹配,而不会从自我报告中丢失信息.
  • 数字表型结合适当的ML技术可以增强精神病学临床实践.