Construction method and application of morphological fusion classification index of neuroimaging markers

Abstract

The present invention is a construction method and application of neuroimaging marker morphological fusion classification index. The construction method includes the following contents: obtaining structural MRI data of M centers, extracting brain structural image characteristic data; performing independent training with the data of each center, respectively Establish the classification model of each center and obtain the classification model of M centers; for any sample, in the classification model of all centers, calculate the classification weight value of the sample in each feature of each model, that is, the SHAP matrix; then use each The sample size used in the training of each model is the weight, and a single morphological fusion classification index MICI value is obtained by calculating according to formula (1): where Si represents the sample size of model i, B represents the total number of features, and a i Represents the SHAP value of feature a in model i, i=1~M. The MICI value can well realize the discrimination between patients with mental illness and normal people, and has good interpretability, evolution and scalability.

Description

technical field [0001] The invention relates to the field of neuroimaging markers, and provides a method for constructing a neuroimaging marker based on machine learning and multi-center data-morphological fusion classification index (MICI value), which is used to assist individualized diagnosis and treatment of neuropsychiatric diseases. Background technique [0002] Neuropsychiatric diseases characterized by diffuse brain damage, such as schizophrenia, major depression, and Alzheimer's disease, seriously affect human health and bring a huge burden to individuals and society. At present, mental illness mainly relies on the subjective diagnosis of clinical symptoms by doctors, and there are certain misdiagnoses and missed diagnoses. Magnetic resonance imaging (MRI) has attracted more and more attention due to its advantages of simplicity, non-invasiveness and comprehensiveness. Numerous studies have reported significant differences between the brain structures of patients w...

AI Technical Summary

Problems solved by technology

However, there are some unavoidable problems in multi-center data sharing. For example, the original MRI data analysis needs to consume massive storage, network and computing resources, which greatly increases the human and material costs of the analysis organization; in addition, the original MRI data contains personal identification. information, how to effectively protect the privacy of the subjects is also a difficult problem

The machine learning model is a "black box" with a large number of parameters. The interpretability of the prediction results is relatively poor, and it is difficult to establish human-understandable connections with the neurobiological characteristics and clinical symptoms of mental diseases, which makes it unable to be used in clinical work. Get fast promotion and conversion

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