公开(公告)号：US20230225649A1

公开(公告)日：2023-07-20

申请号：US18125645

申请日：2023-03-23

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Chaoliang SUN ,  Zhichao WANG ,  Haotian QIAN ,  Jun LI ,  Jingsong LI

IPC: A61B5/16 ,  A61B5/00

CPC classification number: A61B5/16 ,  A61B5/7264

Abstract: Disclosed is a graph model-based brain functional alignment method. The method includes: mapping high-dimensional functional brain imaging data to a two-dimensional time-series matrix by taking brain functional activity signals of a subject under a specific cognitive function state as input , constructing a model based on graph convolutional networks to distinguish different cognitive function states, generating a brain activation distribution priori graph by a meta analysis method to assist in predicting a specific brain function activation mode of each subject, combining the two to map functional brain imaging data of each subject to a shared representation space applicable to a large-scale group, and finally achieving accurate brain function alignment between subjects. According to the method, graph representation information generated in the shared representation space can also be used for accurately predicting the brain function state and behavioral index of the subjects.

公开(公告)号：US20240078678A1

公开(公告)日：2024-03-07

申请号：US18360796

申请日：2023-07-27

Applicant: ZHEJIANG LAB

Inventor： Jingsong LI ,  Jun LI ,  Baochen WANG ,  Zhuoxin LI ,  Yu TIAN ,  Tianshu ZHOU

IPC: G06T7/00

CPC classification number: G06T7/0014 ,  G06T2207/10088 ,  G06T2207/30016

Abstract: The present application discloses a system and a device for functional connectivity matrix processing based on feature selection using a filtering method, which comprises the following steps: acquiring a preprocessed resting state brain functional magnetic resonance image of a subject; extracting time series; calculating a Pearson correlation coefficient to obtain a Pearson correlation coefficient matrix; vectorizing the Pearson correlation coefficient matrix; calculating quantitative correlation indices using a filtering method, and selecting a quantitative correlation index based on a preset threshold; performing weighting processing a selected functional connectivity feature by using the corresponding quantitative correlation index with high correlation with a disease diagnosis result to obtain a functional connectivity matrix; and obtaining a prediction result from the functional connectivity matrix.

公开(公告)号：US20230290514A1

公开(公告)日：2023-09-14

申请号：US18129754

申请日：2023-03-31

Applicant: ZHEJIANG LAB

Inventor： Yu ZHANG ,  Jun LI ,  Chaoliang SUN ,  Huan ZHANG ,  Zhichao WANG ,  Jingsong LI

IPC: G16H50/30 ,  G06T7/00 ,  G06V10/62 ,  G06F17/16

CPC classification number: G16H50/30 ,  G06T7/0012 ,  G06V10/62 ,  G06F17/16 ,  G06T2207/10088 ,  G06T2207/30016

Abstract: Disclosed are a disease prediction method, system and apparatus based on a multi-relation functional connectivity matrix. A Pearson correlation coefficient matrix and a DTW distance matrix are respectively calculated according to resting state functional magnetic resonance time series extracted from a brain atlas, the DTW distance matrix is converted in combination with the Pearson correlation coefficient matrix into a DTW′ matrix which includes correlation degree and correlation direction information and whose numerical range is equivalent to the value range of a Pearson coefficient, and a functional connectivity matrix is obtained after weighted combination. The present disclosure combines DTW distance information to weaken the dynamic change of functional connectivity and the influence of asynchrony of functional signals in different brain regions on the functional connectivity matrix, so that the calculated functional connectivity matrix can better reflect the correlation between the functional signals in different brain regions.