WO-2026092766-A1 - METHOD AND APPARATUS FOR DERIVING EFFECTIVE CONNECTIVITY INFORMATION, METHOD AND APPARATUS FOR TRAINING DIGITAL TWIN BRAIN MODEL, AND DEVICE

WO2026092766A1WO 2026092766 A1WO2026092766 A1WO 2026092766A1WO-2026092766-A1

Abstract

Disclosed in the present application are a method and apparatus for deriving effective connectivity information, a method and apparatus for training a digital twin brain model, and a device. The method comprises: determining perturbation data of a first brain node among at least two brain nodes and brain neural data of the at least two brain nodes in a time series; by means of a digital twin brain model, and, on the basis of the perturbation data of the first brain node and the brain neural data of the at least two brain nodes in the time series, obtaining first predicted neural data of the at least two brain nodes at the next moment; and, on the basis of the first predicted neural data of the at least two brain nodes at the next moment and second predicted neural data of the at least two brain nodes at the next moment, determining brain effective connectivity information from the first brain node to a second brain node. On the basis of a training data set, a time series prediction network is trained to obtain the digital twin brain model.

Inventors

LIU, QUANYING
PENG, Kaining
LUO, Zixiang
Liang, Zhichao

Assignees

南方科技大学

Dates

Publication Date: 20260507
Application Date: 20251104
Priority Date: 20241104

Claims (18)

A method for deriving information about the brain's effective connections, the method comprising: Determine the perturbation data of a first brain node out of at least two brain nodes and the brain neural data of the at least two brain nodes in a time series; wherein the perturbation data of the first brain node includes an element with a non-zero value in a virtual perturbation vector; Using a digital twin brain model, based on the perturbation data of the first brain node and the brain neural data of the at least two brain nodes in the time series, the first predicted neural data of the at least two brain nodes at the next time step is obtained; wherein, the digital twin brain model is obtained by training based on a time series prediction network; Based on the first predicted neural data of the at least two brain nodes at the next time step and the second predicted neural data of the at least two brain nodes at the next time step, the effective brain connection information from the first brain node to the second brain node is determined; wherein, the second brain node is the other brain node among the at least two brain nodes besides the first brain node; the second predicted neural data represents the predicted data obtained without adding the perturbation data.
The method according to claim 1, wherein the method further comprises: Using the digital twin brain model, based on the brain neural data of the at least two brain nodes in the time series, the second predicted neural data of the at least two brain nodes at the next time step is obtained.
According to the method of claim 1 or 2, wherein the effective brain connectivity information includes effective connectivity types, and determining the effective brain connectivity information from the first brain node to the second brain node based on the first predicted neural data of the at least two brain nodes at the next time step and the second predicted neural data of the at least two brain nodes at the next time step includes: If the first predicted neural data of the second brain node at the next time step is greater than the second predicted neural data of the second brain node at the next time step, the effective connection type is determined to be excitatory. If the first predicted neural data of the second brain node at the next time step is less than the second predicted neural data of the second brain node at the next time step, the effective connection type is determined to be inhibitory.
The method according to any one of claims 1-3, wherein the brain neural data of the at least two brain nodes in the time series includes brain neural data of the at least two brain nodes corresponding to a preset time length p+1; wherein the brain neural data corresponding to the preset time length p+1 includes brain neural data from time t-p to time t, where p is an integer greater than or equal to 0 and t is an integer greater than p. The method involves obtaining first predicted neural data for the at least two brain nodes at the next time step using a digital twin brain model, based on perturbation data of the first brain node and neural data of the at least two brain nodes within a time series. This includes: Based on the perturbation data of the first brain node and the brain neural data of the at least two brain nodes at time t, determine the perturbation-resolved brain neural data of the at least two brain nodes at time t. Using the digital twin brain model, based on the perturbed brain neural data of the at least two brain nodes at time t, and the brain neural data of the at least two brain nodes from time t-p to time t-1, the first predicted neural data of the at least two brain nodes at time t+1 is obtained.
According to the method of claim 4, wherein determining the perturbed brain neural data of the at least two brain nodes at time t based on the perturbation data of the first brain node and the brain neural data of the at least two brain nodes at time t includes: The perturbation data of the first brain node and the brain neural data of the at least two brain nodes at time t are superimposed to obtain the perturbation-resolved brain neural data of the at least two brain nodes at time t.
According to the method of claim 4, wherein obtaining second predicted neural data of the at least two brain nodes at the next time step based on the brain neural data of the at least two brain nodes in the time series using a digital twin brain model includes: Using the digital twin brain model, based on the brain neural data of the at least two brain nodes from time t-p to time t, the second predicted neural data of the at least two brain nodes at time t+1 is obtained.
The method according to any one of claims 1-6, wherein the effective brain connectivity information includes effective connectivity directions, and determining the effective brain connectivity information from the first brain node to the second brain node includes: The effective connection direction is defined as from the first brain node to the second brain node.
The method according to any one of claims 1-7, wherein the brain effective connectivity information includes effective connectivity types, and determining the brain effective connectivity information from the first brain node to the second brain node based on the first predicted neural data of the at least two brain nodes at the next time step and the second predicted neural data of the at least two brain nodes at the next time step includes: If the first predicted neural data of the second brain node at time t+1 is greater than the second predicted neural data of the second brain node at time t+1, the effective connection type is determined to be excitatory. If the first predicted neural data of the second brain node at time t+1 is less than the second predicted neural data of the second brain node at time t+1, the effective connection type is determined to be inhibitory.
The method according to any one of claims 1-8, wherein the effective brain connectivity information includes effective connectivity strength, and determining the effective brain connectivity information from the first brain node to the second brain node based on the first predicted neural data of the at least two brain nodes at the next time step and the second predicted neural data of the at least two brain nodes at the next time step includes: Determine the strength information of the first predicted neural data of the second brain node at time t+1, and determine the strength information of the second predicted neural data of the second brain node at time t+1; The effective connection strength is determined based on the strength information of the first predicted neural data and the strength information of the second predicted neural data.
The method according to any one of claims 1-9, wherein determining the perturbation data of the first brain node among at least two brain nodes comprises: The perturbation data of the first brain node is set based on a preset perturbation intensity; The method further includes: Traverse the at least two brain nodes and determine the effective brain connection information between any two brain nodes to obtain the whole-brain effective connection of the at least two brain nodes.
The method according to any one of claims 1-10, wherein the method further comprises: Using the digital twin brain model, based on multiple perturbation data of the first brain node corresponding to multiple times and brain neural data of the at least two brain nodes in the time series, multiple first predicted neural data of the at least two brain nodes corresponding to the multiple times are obtained respectively. Using the digital twin brain model, based on the brain neural data of the at least two brain nodes in the time series, multiple second predicted neural data corresponding to the at least two brain nodes at the multiple times are obtained respectively; Based on the plurality of first predictive neural data and the plurality of second predictive neural data, determine the plurality of valid brain connections from the first brain node to the second brain node, corresponding to the plurality of time points; Based on the multiple valid brain connectivity information, the target valid connectivity information from the first brain node to the second brain node is determined.
A method for training a digital twin brain model, the method comprising: A time-series prediction network is trained based on a training dataset to obtain a digital twin brain model; wherein, the digital twin brain model is used to predict the neural data of the next time step based on the brain neural data of the brain nodes in the time series; the digital twin brain model is used to perform the method for deriving effective brain connectivity information as described in any one of claims 1-11; The training dataset includes neural training data for at least two brain nodes corresponding to a preset time length p+1; wherein, the neural training data corresponding to the preset time length p+1 includes neural training data from time m-p to time m, and the training dataset also includes neural training data for the at least two brain nodes at time m+1; p is an integer greater than or equal to 0, and m is an integer greater than p. The process of training a time-series prediction network based on a training dataset to obtain a digital twin brain model includes: Using the time series prediction network, based on the neural training data of the at least two brain nodes from time m-p to time m, the prediction data of the at least two brain nodes at time m+1 is obtained; Based on the prediction data of the at least two brain nodes at time m+1 and the neural training data of the at least two brain nodes at time m+1, the time series prediction network is modified to obtain the digital twin brain model.
According to the method of claim 12, wherein the step of correcting the time-series prediction network based on the prediction data of the at least two brain nodes at time m+1 and the neural training data of the at least two brain nodes at time m+1 to obtain the digital twin brain model comprises: The model parameters are determined based on the prediction data of the at least two brain nodes at the (m+1)th time, the neural training data of the at least two brain nodes at the (m+1)th time, and the preset time length corresponding to the training dataset. The digital twin brain model is determined based on the model parameters.
The method according to claim 12 or 13, wherein training the time series prediction network based on the training dataset to obtain the digital twin brain model includes: Using the time series prediction network, based on the training dataset, multiple predicted data corresponding to multiple time points for the at least two brain nodes are obtained; Based on the multiple prediction data and the neural training data of the at least two brain nodes corresponding to the multiple time points, multiple data errors corresponding to the multiple time points are determined; The model parameters are determined based on the aforementioned multiple data errors; The digital twin brain model is determined based on the model parameters.
The method according to any one of claims 12-14, wherein the method further comprises: Based on the training dataset and the prediction dataset corresponding to the training dataset, the determination coefficient corresponding to the digital twin brain model is determined; Using the digital twin brain model, based on the brain neural data and random noise, the first functional connectivity matrix predicted by the digital twin brain model is determined, and the matrix correlation coefficient between the first functional connectivity matrix and the second functional connectivity matrix corresponding to the training dataset is determined. Based on the determination coefficient and the matrix correlation coefficient, the model performance parameters of the digital twin brain model are determined.
The method according to any one of claims 12-15, wherein the training dataset comprises neural training data for at least two brain nodes corresponding to T time points, the prediction dataset comprises prediction data for the at least two brain nodes corresponding to T time points, where T is an integer greater than 0, and determining the determination coefficients corresponding to the digital twin brain model based on the training dataset and the prediction dataset corresponding to the training dataset comprises: Based on the neural training data of the at least two brain nodes corresponding to T time points, determine the average data of the at least two brain nodes corresponding to T time points; Based on the mean of the data, the neural training data corresponding to T time points, and the prediction data corresponding to T time points, the determination coefficient corresponding to the digital twin brain model is determined.
A device for deriving effective brain connectivity information, the device comprising: A determining unit is configured to determine perturbation data of a first brain node among at least two brain nodes and brain neural data of the at least two brain nodes in a time series; wherein the perturbation data of the first brain node includes an element with a non-zero value in a virtual perturbation vector; The acquisition unit is used to obtain the first predicted neural data of the at least two brain nodes at the next moment based on the perturbation data of the first brain node and the brain neural data of the at least two brain nodes in the time series using a digital twin brain model; wherein, the digital twin brain model is obtained by training based on a time series prediction network; The determining unit is configured to determine effective brain connectivity information between the first brain node and the second brain node based on the first predicted neural data of the at least two brain nodes at the next time step and the second predicted neural data of the at least two brain nodes at the next time step; wherein, the second brain node is the other brain node among the at least two brain nodes besides the first brain node; the second predicted neural data represents the predicted data obtained without adding the perturbation data.
A computer device, the computer device comprising: a processor and a memory; wherein, The memory is used to store computer programs that can run on the processor; The processor is configured to, when running the computer program, perform the method as described in any one of claims 1-11 or 12-16.

Description

Effective methods for deriving information, training methods, devices, and equipment for digital twin brain models. Cross-reference of related applications This application claims priority to Chinese Patent Application No. 202411569048.4, filed on November 4, 2024, entitled “Method for Derivation of Effective Connection Information, Method, Apparatus, and Device for Training Digital Twin Brain Model”, the entire contents of which are incorporated herein by reference. Technical Field This invention relates to the field of brain neuroscience technology, and in particular to a method for deriving effective brain connectivity information, a training method for digital twin brain models, and related devices and equipment. Background Technology Effective connectivity (EC) characterizes causal interactions between brain regions and is fundamental to understanding brain information processing. EC can be obtained through experimental and data-driven methods. However, common experimental methods are not applicable to stimulation and observation across the entire human brain; common data-driven methods for inferring effective connectivity are computationally complex, with model-based methods being particularly difficult to compute, while model-free methods can only distinguish the existence of directed connections. It is evident that neither common experimental methods nor data-driven approaches can obtain accurate information about effective brain connectivity. Summary of the Invention This application provides a method for deriving effective brain connectivity information, a method for training a digital twin brain model, an apparatus, and a device that can obtain accurate effective brain connectivity information. The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a method for deriving effective brain connectivity information, the method comprising: Determine the perturbation data of the first brain node among at least two brain nodes and the brain neural data of the at least two brain nodes in the time series; Using a digital twin brain model, based on the perturbation data of the first brain node and the brain neural data of the at least two brain nodes in the time series, the first predicted neural data of the at least two brain nodes at the next time step is obtained; wherein, the digital twin brain model is obtained by training based on a time series prediction network; Based on the first predicted neural data of the at least two brain nodes at the next time step and the second predicted neural data of the at least two brain nodes at the next time step, the effective brain connection information from the first brain node to the second brain node is determined; wherein, the second brain node is the other brain node among the at least two brain nodes besides the first brain node; the second predicted neural data represents the predicted data obtained without adding the perturbation data. In the embodiments of this application, based on a digital twin brain model obtained through training, the brain's response to perturbation stimuli is predicted and effective brain connectivity information is inferred by adding perturbation data to brain nodes. The digital twin brain model is obtained through training on a time-series prediction network; therefore, it can effectively and accurately predict the dynamic changes in brain neural data, thereby obtaining more accurate information on effective brain connectivity. Secondly, embodiments of this application provide a method for training a digital twin brain model, the method comprising: A time-series prediction network is trained based on a training dataset to obtain a digital twin brain model; wherein, the digital twin brain model is used to predict the neural data of the next time step based on the brain neural data of the brain nodes in the time series. The training dataset includes neural training data for at least two brain nodes corresponding to a preset time length p+1; wherein, the neural training data corresponding to the preset time length p+1 includes neural training data from time m-p to time m, and the training dataset also includes neural training data for the at least two brain nodes at time m+1; p is an integer greater than or equal to 0, and m is an integer greater than p. The process of training a time-series prediction network based on a training dataset to obtain a digital twin brain model includes: Using the time series prediction network, based on the neural training data of the at least two brain nodes from time m-p to time m, the prediction data of the at least two brain nodes at time m+1 is obtained; Based on the prediction data of the at least two brain nodes at time m+1 and the neural training data of the at least two brain nodes at time m+1, the time series prediction network is modified to obtain the digital twin brain model. In the embodiments of this appl