CN-121997228-A - Method for identifying brain function connection of endometriosis based on stage driving

CN121997228ACN 121997228 ACN121997228 ACN 121997228ACN-121997228-A

Abstract

The invention discloses a brain function connection identification method based on stage driving, which breaks through the limitation that a traditional model cannot describe a central regulation mechanism from a disease state through a brain function feedback identification path taking rASRM stage states as driving variables, and definitely identifies central feedback signals of stage dependence of different stages through systematic comparison of feedback connection modes under different stage states, thereby providing an explanation basis for the association between clinical classification of endometriosis and central symptom expression.

Inventors

GENG RONG
ZHAO LIJIE
HU YIRU

Assignees

佛山市妇幼保健院

Dates

Publication Date: 20260508
Application Date: 20260123

Claims (3)

1. A method for identifying endometriosis brain function connection based on stage driving, which is characterized by comprising the following steps: Acquiring whole genome associated data of a target crowd as a source of an exposure variable, acquiring an EMs stage result corresponding to the target crowd, unifying the whole genome associated data and the EMs stage result to the same reference genome version, and then carrying out SNP locus matching and allele alignment treatment; Acquiring resting state functional magnetic resonance scanning data, extracting a brain functional connection phenotype from the resting state functional magnetic resonance scanning data, and carrying out standardized processing on the brain functional connection phenotype, wherein the brain functional connection phenotype at least comprises brain section connection characteristics and brain section node activity indexes; screening tool variables which are obviously related to single nucleotide polymorphism sites from the corresponding whole genome associated data respectively aiming at the EMs stage state of each class, and constructing an independent effective tool variable set after preprocessing the tool variables; Taking the EMs stage result as an exposure variable, taking the brain function connection phenotype as a final variable, constructing three independent causal models, respectively corresponding to the stage stages of the EMs, adopting an inverse variance weighting method as a main analysis strategy, evaluating whether the genetic burden of the EMs has significant change on a specific brain connection structure or node activity level, recording the brain section connection characteristics which are significantly changed under different stage stages of the EMs, comparing the stability and the variability of the brain section connection characteristics among different stage stages of the EMs, and further identifying central feedback signals influenced by the dependency or the continuity of each stage; By transversely comparing the variation modes of the affected brain function connection phenotypes in different EMs stage by stage, identifying common variation modes and stage specific channel combinations of different stage by stage, constructing a disease-brain connection feedback network according to the common characteristics and the specific channels, and excavating key regulation and control nodes which are probability involved in chronic pain regulation, emotion control or central integration functions by combining the connection direction and brain region function attributes to form candidate intervention target reserve.
2. The method for identifying a brain function connection based on stage driving endometriosis according to claim 1, wherein said standardized processing of the brain function connection phenotype comprises: And uniformly registering the brain interval connection characteristics and the brain region node activity indexes by adopting a predefined brain network template, wherein the predefined brain network template covers a default mode network, an attention network, a central execution network, a sensory-motor system, a cerebellum network and an edge system.
3. The method for identifying a phase-driven endometriosis brain function connection as defined in claim 1, wherein the preprocessing of the tool variables comprises: and performing linkage disequilibrium pruning treatment on the tool variables by using a PLINK tool, setting pruning parameters to r2<0.001 and window width of 10Mb, reserving single nucleotide polymorphism sites with highest significance in each linkage disequilibrium block, and after removing low-frequency variation sites with minor allele frequency less than 0.01, unifying reference allele directions to obtain a group of mutually independent effective tool variable sets.

Description

Method for identifying brain function connection of endometriosis based on stage driving Technical Field The invention relates to the technical field of medical image and biological information analysis, in particular to a brain function connection identification method based on stage driving for endometriosis. Background Endometriosis (Endometriosis, EMs) is a chronic disease characterized by ectopic growth of endometrial-like tissues, and focal extent (stage I-IV) is clinically assessed by the rASRM staging system to guide intraoperative staging and postoperative management. However, the staging system cannot accurately predict central manifestations of chronic pain, anxiety depression and the like of patients, and suggests that the central manifestations do not cover systemic regulatory dimensions of diseases. Studies have suggested that EMs patients have abnormal resting connection patterns in the brain regions such as the islets, anterior cingulate cortex, anterior wedge lobes, thalamus, etc. However, most current studies are based on transection observations, and it is not clear whether different staging levels of the disease burden can contribute to brain function, especially whether there is a hierarchical regulation of the central network or a structural remodeling mechanism during disease progression. There is currently no framework for systematic assessment of whether the "EMs staging status can affect brain connectivity through genetic burden feedback" nor is there any study to reveal whether central sensitization or inhibition pathways in the course of disease progression are genetically cumulative or stage dependent. Therefore, there is a need to construct a causal model based on public genetic-image data, identify the potential feedback path of rASRM phases to brain function connections, and provide a theoretical basis for explaining the central adaptation mechanisms of EMs and personalized central intervention strategies. Disclosure of Invention Aiming at the problems, the invention provides a brain function connection identification method based on stage driving for endometriosis, which mainly solves the problems of the background technology. In order to solve the technical problems, the technical scheme of the invention is as follows: A method for identifying endometriosis brain function connection based on stage driving, comprising the following steps: Acquiring whole genome associated data of a target crowd as a source of an exposure variable, acquiring an EMs stage result corresponding to the target crowd, unifying the whole genome associated data and the EMs stage result to the same reference genome version, and then carrying out SNP locus matching and allele alignment treatment; Acquiring resting state functional magnetic resonance scanning data, extracting a brain functional connection phenotype from the resting state functional magnetic resonance scanning data, and carrying out standardized processing on the brain functional connection phenotype, wherein the brain functional connection phenotype at least comprises brain section connection characteristics and brain section node activity indexes; screening tool variables which are obviously related to single nucleotide polymorphism sites from the corresponding whole genome associated data respectively aiming at the EMs stage state of each class, and constructing an independent effective tool variable set after preprocessing the tool variables; Taking the EMs stage result as an exposure variable, taking the brain function connection phenotype as a final variable, constructing three independent causal models, respectively corresponding to the stage stages of the EMs, adopting an inverse variance weighting method as a main analysis strategy, evaluating whether the genetic burden of the EMs has significant change on a specific brain connection structure or node activity level, recording the brain section connection characteristics which are significantly changed under different stage stages of the EMs, comparing the stability and the variability of the brain section connection characteristics among different stage stages of the EMs, and further identifying central feedback signals influenced by the dependency or the continuity of each stage; By transversely comparing the variation modes of the affected brain function connection phenotypes in different EMs stage by stage, identifying common variation modes and stage specific channel combinations of different stage by stage, constructing a disease-brain connection feedback network according to the common characteristics and the specific channels, and excavating key regulation and control nodes which are probability involved in chronic pain regulation, emotion control or central integration functions by combining the connection direction and brain region function attributes to form candidate intervention target reserve. In some embodiments, the normalization process of the brain function con