Magnetic resonance imaging (MRI) based brain disease individual prediction method and system

Abstract

The invention discloses a magnetic resonance imaging (MRI) based brain disease individual prediction method and a magnetic resonance imaging (MRI) based brain disease individual prediction system. The method comprises the following steps: 1: obtaining the MRI of the brain of a patient with mental diseases; 2: carrying out denoising and dimension reduction treatment on the MRI of the brain of the patient; 3: carrying out feature selection by utilizing a ReliefF algorithm; 4: adaptively obtaining a spatial brain area by using a spatial cluster analysis method; 5: removing redundant features by utilizing a correlation-based feature selection algorithm, thus obtaining an optimal feature subset; 6: carrying out multiple linear regression analysis based on the optimal feature subset to recognize potential biomarkers. The method has the beneficial effects that the embodiment of the invention integrates various machine learning methods and can rapidly and conveniently achieve quantitative and individual accurate prediction of the interest features of mental diseases, such as clinical indexes, based on various image data in different mode types, thus being beneficial to understanding the brain structures, function abnormity and potential pathogenesis of the diseases.

Description

technical field [0001] Embodiments of the present invention relate to the technical fields of biological information and computational medicine, and in particular to a method and system for individualized prediction of brain diseases based on magnetic resonance images. Background technique [0002] With the development of economy, health and medical level, the average life expectancy of people all over the world has been extended. But at the same time, due to many factors such as increased competitive pressure, the incidence of mental illness is increasing year by year worldwide, and it has become one of the main causes of death[1] (van Waarde et al.A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry 20, 609-614, 2015). Clinically, people's understanding of mental illness is mainly through magnetic resonance imaging (MRI). As a non-invasive imaging technique, it has greatly advanced the u...

AI Technical Summary

Problems solved by technology

[0004] However, existing algorithms often divide brain regions based on fixed, predefined templates (such as AAL template) and use them as input features for predictive models, which largely limits the search for more accurate, predictive models. Performance brain area of ​​interest (predictor, regions of interest, ROI). Because the brain area (ROI) that plays a key predictive role in actual situations is likely to be composed of multiple partitions obtained by pre-determined template division

Most of the existing research models use a single data type or a single feature selection algorithm, which cannot be effectively extended to the prediction tasks of uncertain factors such as multiple modal image data types and different target metrics. It has limited its promotion in various types of disease diagnosis and imaging modality, and its repeatability is poor for complex disease research.

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