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Image correlation detection method based on time sequence sparse regression and additive model

A technique of sparse regression and correlation detection, applied in the field of genetic effects

Pending Publication Date: 2021-04-06
SOUTHERN MEDICAL UNIVERSITY
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  • Image correlation detection method based on time sequence sparse regression and additive model
  • Image correlation detection method based on time sequence sparse regression and additive model
  • Image correlation detection method based on time sequence sparse regression and additive model

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Embodiment 1

[0066] An image correlation detection method based on temporal sparse regression and an additive model, comprising the following steps:

[0067] Step 1. Collect MRI images of multiple target brain regions at different time points and the corresponding genetic data of the target;

[0068] Step 2, respectively preprocessing the MRI images to obtain the processed MRI images, performing quality control and screening on the genetic data to obtain the processed genetic data;

[0069] Step 3, substituting the processed genetic data and the processed MRI image into the objective function based on time series group sparse regression and additive model;

[0070] Step 4: Solve the objective function by Alternating Convex Search Method, and obtain the smooth function of SNP and phenotype with respect to time and the weight of ROI.

[0071] Among them, the specific steps of step two are:

[0072] Step 2.1, respectively preprocessing the MRI image to obtain the processed MRI image, such a...

Embodiment 2

[0117] An image correlation detection method based on time-series sparse regression and additive model, first download the longitudinal data of T1-weighted MRI images of ADNI 1 from the ADNI database (the time points are baseline, 6 months, 12 months and 24 months respectively) , and then select 202 Alzheimer's disease (AD) candidate genes from the AlzGene database. The preprocessing method of each MRI image and genetic data in the database is described in detail below.

[0118] Step 1. Download the MRI images and genetic data from the ADNI database.

[0119] Step 2, preprocessing each MRI image to obtain a processed MRI image, and at the same time performing quality control and screening on the genetic data corresponding to the MRI image to obtain processed genetic data;

[0120] Step 2.1.1, by using the MIPAV software to carry out the correction of the front joint and the back joint;

[0121] Step 2.1.2. Apply a robust decapping algorithm to remove the skull and distort th...

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Abstract

The invention discloses an image correlation detection method based on time sequence sparse regression and an additive model. The method comprises four steps. According to the method, a plurality of weights related to the time pattern smoothing function and ROI are obtained through the four steps, so that correlations between the brain region time progress track and the regional difference of the phenotype are obtained by describing the specific region through the contribution of a plurality of SNPs to the phenotype. The minimum root-mean-square error RMSE is used as an evaluation index for judging whether a specific brain area degeneration progress track is matched with the model or not, the RMSE of the method is 0.15, the RMSE of a model based on a time sequence sparse additive model in the prior art is 1.14, the RMSE of a sparse additive model and the RMSE of a group sparse additive model are 1.33, and therefore the effect is better.

Description

technical field [0001] The invention relates to the technical field of genetic effect based on genetic variable group structure information and its time-varying genetic effect, in particular to an image correlation detection method based on time series sparse regression and additive model. Background technique [0002] Image genetics research based on time series sparse additive model (TV-GroupSpAM) considers the application of longitudinal data, and considers the genetic effect of genetic data changing over time, and performs association analysis between genetic data and single image phenotype data to detect heritability. Important biomarkers of neural change. Moreover, there is a structural association between genetic data, and multiple phenotypic data can play different roles in the process of neural change. Therefore by taking this information into account the accuracy and reliability of detecting important biomarkers can be improved and associated phenotypes can be det...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T5/00G16B20/20G06T9/00
CPCG06T7/0012G06T7/11G16B20/20G06T9/00G06T2207/10088G06T2207/30016G06T5/80Y02A90/10
Inventor 黄美燕冯前进陈秀美
Owner SOUTHERN MEDICAL UNIVERSITY
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