CN-121982335-A - Brain function gradient analysis method, device, medium and program product

CN121982335ACN 121982335 ACN121982335 ACN 121982335ACN-121982335-A

Abstract

The application belongs to the field of brain image data processing, and particularly relates to a brain function gradient analysis method, equipment, medium and program product. The brain function gradient analysis method comprises the steps of obtaining a gradient space of a first function network and a gradient space of a second function network, wherein the first function network and the second function network are the same function network of different brains or different function networks of the same brain, the function networks are a set comprising at least two brain areas which are functionally related to each other, and calculating a bulldozer distance between the gradient space of the first function network and the gradient space of the second function network to obtain a gradient interval. The method adopts the bulldozer distance to capture the spatial characteristics of shape, range, dispersion and the like, realizes the full mining of image data, predicts the post-traumatic stress disorder based on the brain functional gradient analysis method after the bulldozer distance is improved, and can more effectively identify the post-traumatic stress disorder.

Inventors

ZHONG YUAN
WU JIANZE

Assignees

南京师范大学

Dates

Publication Date: 20260505
Application Date: 20260408

Claims (10)

1. A method of brain functional gradient analysis, the method comprising: Acquiring a gradient space of a first functional network and a gradient space of a second functional network, wherein the first functional network and the second functional network are the same functional network of different brains or different functional networks of the same brain, and the functional networks are a set comprising at least two brain areas which are functionally related to each other; and calculating the bulldozer distance between the gradient space of the first functional network and the gradient space of the second functional network to obtain the gradient interval.
2. The brain function gradient analysis method according to claim 1, wherein when the number of brain regions included in the first function network and the second function network is the same, a bulldozer distance between a gradient space of the first function network and a gradient space of the second function network is calculated to obtain a gradient pitch, and the bulldozer distance is expressed as: ; Wherein, the Indicating the distance of the bulldozer, Representing the gradient space of the first functional network, Representing the gradient space of the second functional network, And The number of brain regions contained is the same, min () represents the minimum, Representing slave Conversion to Is provided for the set of active transfer plans of (c), Represents any one of a set of active transfer plans, Is that And M is the matrix inner product of And A transition cost matrix between.
3. The brain function gradient analysis method according to claim 2, wherein when the number of brain regions included in the first function network and the second function network are different, calculating a partial bulldozer distance between the gradient space of the first function network and the gradient space of the second function network to obtain a gradient pitch, the partial bulldozer distance being expressed as: ; Wherein, the Representing a portion of bulldozer distance between the gradient space of the first functional network and the gradient space of the second functional network, Representing the gradient space of the first functional network, Representing the gradient space of the second functional network, And The number of brain regions involved is different, Representing a partial transfer plan feasible in the total transfer plan, M being And A matrix of transfer costs between the two, Is that And M, min () represents the minimum value.
4. A brain function gradient analysis method according to claim 3, wherein the transfer cost matrix is a transfer cost required for converting the gradient space of the first function network into the gradient space of the second function network, and the element of the ith row and the jth column in the transfer cost matrix represents a transfer cost for converting the gradient coordinates of the ith cortex partition in the gradient space of the first function network into the gradient coordinates of the jth cortex partition in the gradient space of the second function network, and the transfer cost represents a euclidean distance between the gradient coordinates of the ith cortex partition in the gradient space of the first function network and the gradient coordinates of the jth cortex partition in the gradient space of the second function network; The element of the ith row and jth column of the transfer plan represents a transfer quality that converts the gradient coordinates of the ith cortical partition in the gradient space of the first functional network to the gradient coordinates of the jth cortical partition in the gradient space of the second functional network.
5. The brain function gradient analysis method according to claim 1, wherein the acquisition method of the gradient space of the first function network includes: Acquiring a first tested functional magnetic resonance image; Performing functional connection analysis based on the functional magnetic resonance image to obtain a first brain functional connection matrix; Performing dimension reduction and gradient calculation on the first brain function connection matrix to obtain a first low-dimension gradient space; And extracting coordinates on a set gradient of a first functional network in the first low-dimensional gradient space to obtain a gradient space of the first functional network.
6. The brain function gradient analysis method according to claim 5, wherein the set gradient is a set The set gradient represents a gradient index over a low-dimensional gradient space, and D is the number of gradients contained in the low-dimensional gradient space.
7. A method of predicting post-traumatic stress disorder, the method comprising: Acquiring a functional magnetic resonance image of a person to be tested; Performing functional connection analysis based on the functional magnetic resonance image to obtain a brain functional connection matrix; performing dimension reduction and gradient calculation on the brain function connection matrix to obtain a low-dimensional gradient space, wherein the low-dimensional gradient space comprises a first gradient, a second gradient and a third gradient; extracting gradient spaces on set gradients of at least two functional networks based on the low-dimensional gradient space to obtain a gradient space of a first functional network and a gradient space of a second functional network, wherein the set gradients are third gradients; calculating a gradient spacing according to the method of any one of claims 1-6 based on the gradient space of the first functional network and the gradient space of the second functional network; inputting the gradient intervals into a classifier to obtain a result of whether the person to be tested has post-traumatic stress disorder.
8. The method of claim 7, wherein the gradient spacing comprises one or more of a gradient spacing between a default mode network and a visual network, a gradient spacing between a default mode network and a dorsal attention network, a gradient spacing between a default mode network and a ventral attention network, a gradient spacing between a default mode network and a sensory motor network, a gradient spacing between a default mode network and an edge network, a gradient spacing between a visual network and a dorsal attention network, a gradient spacing between a visual network and a ventral attention network, a gradient spacing between a visual network and a sensory motor network, a gradient spacing between a visual network and an edge network, a gradient spacing between a highlight network and a dorsal attention network, a gradient spacing between a ventral attention network and a sensory motor network, a gradient spacing between a dorsal attention network and a ventral attention network, a gradient between a dorsal attention network and a motor network, a gradient between a dorsal attention network and a sensory motor network, a gradient between a dorsal attention network and a lateral attention network, and a sensory motor network.
9. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-8.
10. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, realizes the steps of the method according to any one of claims 1-8.

Description

Brain function gradient analysis method, device, medium and program product Technical Field The present application relates to the field of brain image data processing, and more particularly, to a brain functional gradient analysis method, apparatus, medium, and program product. Background The proposal of the human brain connection group provides a brand new visual angle for understanding the brain nervous system and provides an effective tool for taking the brain as a complete system research. In order to fully mine data in neuro-images, researchers have been working to explore different data analysis modes including static functional connections (static Functional Connectivity, sFC) and dynamic functional connections (dynamic Functional Connectivity, dFC) for functional MRI (fMRI), white matter connections for diffusion MRI, etc., and further explore brain pathological manifestations of neuropsychiatric diseases (e.g., alzheimer's disease, epilepsy, autism, depression, bipolar disorder, post-traumatic stress disorder, etc.) based on these analysis modes. While research methods that have long focused on the measurement of neurological processes (e.g., from functional magnetic resonance imaging, fMRI) and cognition have focused on determining discrete brain regions and modules and their specific functional roles, recent developments in concepts and methodologies have improved data analysis methods to allow mapping of large-scale brain functions to low-dimensional manifold characterizations (manifold representations), also known as gradients (gradients). The study literature "Situating the default-mode network along a principal gradient of macroscale cortical organization"（ Chinese translation name, namely a default mode network is positioned along the main gradient of macroscopic cortex tissues, DOI: 10.1073/pnas.160882113) discloses a typical workflow of gradient identification, and the gradient identification and an analysis method based on the gradient identification are widely applied to the study of neuroscience. Post-traumatic stress disorder (Post-Traumatic Stress Disorder, PTSD) is a mental disorder caused by abnormally strong traumatic events such as war, violent attack, serious accidents or natural disasters, and its core symptoms include wound re-experience, avoidance, negative changes in cognition and emotion, and increased alertness. However, not all individuals experience the same type of traumatic event develop PTSD. Of these, a fraction of individuals experienced Trauma but did not develop significant psychopathological symptoms, or recovered rapidly after only transient symptoms, exhibiting greater mental toughness, and such populations were defined in the study as the tough Trauma exposure control group (Trauma-Exposed Control, TEC). Currently, in clinical practice and scientific research, diagnosis and identification of PTSD relies primarily on interview-based clinical scales (e.g., CAPS-5) and patient self-assessment scales (e.g., PCL-5). These methods are highly dependent on subjective reporting of the patient and experience judgment of the clinician, and are highly subjective. The key dilemma is that PTSD patients share a common experience with TEC individuals-i.e., both are exposed to traumatic events, but the pathophysiological characteristics and mechanisms of both are distinct. This "homologous heterofruit" feature makes identification based solely on clinical manifestations and subjective symptoms exceptionally difficult. Disclosure of Invention One of the main steps of the current analysis method based on gradient recognition is to estimate the spatial separation degree by calculating the euclidean distance between the functional network centroids of the brain, but this method has a disadvantage that the euclidean distance calculation method simplifies the whole network into a single representative point, thereby sacrificing the spatial characteristics of the shape, range, dispersion and the like of the network, which is inconsistent with the objective that the learner desires to fully mine the information in the image data. In view of the above problems, the present application provides a brain function gradient analysis method that uses bulldozer distance to estimate spatial separation instead of euclidean distance widely used in the prior art, thereby achieving sufficient mining of image data. The invention discloses a brain functional gradient analysis method, which comprises the steps of obtaining a gradient space of a first functional network and a gradient space of a second functional network, wherein the first functional network and the second functional network are the same functional network of different brains or different functional networks of the same brain, the functional networks are a set comprising at least two brain areas which are functionally related to each other, and calculating a bulldozer distance between the gradient space of the first functional network