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Schizophrenia

Schizophrenia

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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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Computer-aided diagnosis of schizophrenia based on node2vec and Transformer.

Anan Gan¹, Anmin Gong², Peng Ding¹

¹Faculty of Information Engineering and Automation, Kunming University of Science and Technology, No. 727 Jingming South RD, Kunming, 650031, Yunnan, China; Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, No. 727 Jingming South RD, Kunming, 650031, Yunnan, China.

Journal of Neuroscience Methods

|February 23, 2023

Summary

This study introduces a novel method for diagnosing schizophrenia (SZ) using brain functional networks. By applying node2vec and GridMask with a Transformer model, researchers achieved high accuracy in identifying SZ patients, improving computer-aided diagnosis.

Keywords:

GridMask Schizophrenia Transformer fMRI based brain network node2vec

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Area of Science:

Neuroscience
Computer Science
Medical Imaging

Background:

Schizophrenia (SZ) diagnosis is challenging due to abnormal brain structure and function.
Traditional deep learning models struggle with non-Euclidean brain network data.
Manual feature engineering limits machine learning model performance in SZ detection.

Purpose of the Study:

To develop an effective deep learning approach for aided diagnosis of schizophrenia.
To address the challenge of learning intrinsic features from non-Euclidean brain networks.
To improve the accuracy of computer-aided diagnosis for schizophrenia.

Main Methods:

Constructed brain functional networks from fMRI data.
Applied node2vec for graph embedding to retain network structure.
Utilized GridMask for data augmentation and Transformer models for feature extraction and classification.

Main Results:

Demonstrated significantly lower small-world brain network values in SZ patients compared to healthy controls (p=0.014).
Achieved 97.78% classification accuracy in distinguishing SZ patients from healthy individuals.
Validated the effectiveness of node2vec in learning brain network features for deep learning models.

Conclusions:

The combination of node2vec, GridMask, and Transformer models offers a robust method for SZ detection.
This approach overcomes limitations of general deep learning models in analyzing non-Euclidean brain network data.
The proposed methods show promise for high-precision computer-aided diagnosis of schizophrenia.