Method for removing multi-site functional magnetic resonance imaging heterogeneity for predicting diseases

A functional magnetic resonance and multi-site technology, applied in neural learning methods, biological neural network models, medical informatics, etc., to achieve strong effectiveness, save manpower and time, and improve work efficiency

Pending Publication Date: 2022-08-09
EAST CHINA NORMAL UNIVERSITY
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But this decoupling represents learning constraints, propagating heterogeneity-removing fMRI information across population maps, and combining the site-specific embeddings obtained fro

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  • Method for removing multi-site functional magnetic resonance imaging heterogeneity for predicting diseases
  • Method for removing multi-site functional magnetic resonance imaging heterogeneity for predicting diseases
  • Method for removing multi-site functional magnetic resonance imaging heterogeneity for predicting diseases

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[0034] The present invention will be further described in detail below by taking the prediction of mental illness as an example.

[0035] see Figure 1 to Figure 2 The present invention firstly applies the HO brain atlas to the multi-site resting-state fMRI data to extract the brain network functional connection matrix after Fisher transformation and performs feature engineering, and secondly, the brain network features are decoupled by the double-head encoder and expressed as site-invariant and site-specific embeddings, and train the encoder with four regularization constraints including site classification loss, reconstruction loss, site-specific embedding sparsification constraint, and disease prediction loss. Among them, in the process of disease prediction, a population map is constructed based on site-specific embedding and phenotype information, and the site-invariant embedding is propagated and transformed on the population map through a graph convolutional neural netw...

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Abstract

The invention discloses a method for removing multi-site functional magnetic resonance imaging heterogeneity for predicting diseases. The method is characterized by comprising the following steps: acquiring multi-site resting state fMRI data and a phenotype data set; extracting fMRI data by using an HO brain map, performing Fisher transformation on a brain network function connection matrix, and performing feature engineering; brain network features are decoupled and expressed as site invariant and site specific embedding through a double-end encoder, and the encoder is trained by using regular constraint; constructing a population map based on site specific embedding and phenotypic information, and performing propagation and conversion of site invariant embedding on the population map through a map convolutional neural network; and taking a category with a higher probability in the two-dimensional vector obtained by final conversion from a large number of unlabeled nodes contained in the population map, and the like. Compared with the prior art, the method has a high-accuracy diagnosis effect, errors caused by a large amount of manual intervention are effectively avoided, and the working efficiency and the accuracy of the diagnosis result are greatly improved.

Description

technical field [0001] The invention relates to the technical field of computer-aided diagnosis, in particular to a method for removing heterogeneity of multi-site functional magnetic resonance imaging for predicting diseases. Background technique [0002] Resting-state functional magnetic resonance imaging (rs-fMRI) has the ability to capture the interaction of regions of interest in the brain. The findings suggest that brain functional connectivity patterns can serve as diagnostic biomarkers for a range of psychiatric disorders, including Alzheimer's disease, depression, and autism. Due to the difficulty of diagnosing mental illnesses, computer-aided diagnosis has high hopes. However, in the process of using large-scale shared multi-site fMRI data to train a diagnostic model, the diagnostic model is difficult to achieve the desired effect due to data heterogeneity caused by many differences in acquisition protocols and scanner types used at each site. Therefore, it is of...

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Application Information

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IPC IPC(8): G16H50/20G16H50/30G06N3/04G06N3/08
CPCG16H50/20G16H50/30G06N3/08G06N3/045Y02A90/10
Inventor 胡文心林妍妤蔡建华
Owner EAST CHINA NORMAL UNIVERSITY
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