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Related Concept Videos

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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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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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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Negative and Cognitive Symptoms of Schizophrenia01:30

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Related Experiment Video

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Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
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A kernel machine method for detecting higher order interactions in multimodal datasets: Application to schizophrenia.

Md Ashad Alam1, Hui-Yi Lin2, Hong-Wen Deng3

  • 1Department of Biomedical Engineering, Tulane University, New Orleans, LA 70118, USA.

Journal of Neuroscience Methods
|September 6, 2018
PubMed
Summary
This summary is machine-generated.

A new kernel machine method effectively detects complex interactions in multimodal biological data. This approach identified significant biomarkers for schizophrenia, advancing neurodegeneration research.

Keywords:
Higher order interactionImaging genetics and epigeneticsKernel machine methodsMultimodal datasetsSchizophrenia

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Neuroscience

Background:

  • Technological advancements facilitate the collection of high-resolution multimodal datasets.
  • Analyzing complex interactions within these multimodal datasets presents a significant challenge.

Purpose of the Study:

  • To introduce a novel kernel machine method for detecting higher-order interactions in multimodal biological data.
  • To evaluate the method's efficacy in identifying biomarkers for complex diseases like schizophrenia.

Main Methods:

  • Developed a semiparametric kernel machine approach within a reproducing kernel Hilbert space.
  • Formulated the method as a mixed-effects linear model with a score-based variance component statistic.
  • Tested higher-order interactions between multimodal datasets.

Main Results:

  • The method identified 13 significant triplets involving gene-derived SNPs, Regions of Interest (ROIs), and DNA methylations correlated with hippocampal volume changes.
  • A specific triplet (MAGI2, CRBLCrus1.L, FBXO28) was identified as a strong biomarker for schizophrenia (p-value ≤0.000001).
  • Performance was compared favorably against principal component analysis and sequence kernel association test methods.

Conclusions:

  • The novel kernel machine method is effective for analyzing multimodal data and detecting higher-order interactions.
  • The identified biomarkers offer insights into schizophrenia-related neurodegeneration.
  • This approach is broadly applicable to multimodal data analysis in other disease studies.