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Related Concept Videos

Theoretical Approaches to Psychological Disorder01:29

Theoretical Approaches to Psychological Disorder

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The development of psychological disorders, which are characterized by deviant, maladaptive, and personally distressing behaviors, has been explored through several theoretical approaches.
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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.
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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.
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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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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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Basics of Multivariate Analysis in Neuroimaging Data
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Machine learning approaches: from theory to application in schizophrenia.

Elisa Veronese1, Umberto Castellani2, Denis Peruzzo3

  • 1Scientific Institute IRCCS "Eugenio Medea", San Vito al Tagliamento, 33078 Pordenone, Italy.

Computational and Mathematical Methods in Medicine
|February 4, 2014
PubMed
Summary

Machine learning, specifically support vector machines, aids in diagnosing schizophrenia using neuroimaging data. These advanced techniques offer valuable insights into the biological basis of schizophrenia and potential clinical applications.

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Area of Science:

  • Neuroimaging
  • Psychiatric Research
  • Machine Learning

Background:

  • Machine learning (ML) is increasingly used for neuroimaging data analysis in disease diagnosis.
  • Support vector machine (SVM)-based methods are prominent in psychiatric neuroimaging for schizophrenia investigation.

Purpose of the Study:

  • To provide an overview of recent SVM-based methods for schizophrenia research.
  • To compare the accuracy of ML algorithms in classifying schizophrenia patients versus healthy controls.
  • To assess the utility of automatic classification in understanding schizophrenia's biological underpinnings.

Main Methods:

  • Description of pattern recognition and machine learning terminology.
  • Detailed summary and explanation of implemented SVM algorithms.
  • Comparative analysis of classification accuracy against published studies.

Main Results:

  • SVM methods demonstrate potential in classifying schizophrenia subjects.
  • Accuracy results are compared with other recent studies to benchmark performance.
  • The study highlights the effectiveness of specific ML algorithms in psychiatric neuroimaging.

Conclusions:

  • Automatic classification approaches are valuable tools for understanding schizophrenia's biological basis.
  • ML methods show promise for application in clinical practice for psychiatric disorders.
  • Further research can refine these techniques for improved diagnostic and prognostic capabilities.