Cerebrospinal fluid and multi-brain region signal coupling analysis method, device and equipment

Abstract

The application relates to the technical field of medical image processing and analysis, and discloses a signal coupling analysis method, device and equipment based on cerebrospinal fluid and multiple brain regions. The method comprises the following steps: marking a cerebrospinal fluid region according to a collected BOLD image, and extracting a BOLD signal time sequence of the cerebrospinal fluid region; carrying out tissue structure segmentation according to a collected T1 weighted image to obtain segmented fine-grained tissue structures; calculating cross correlation by using the BOLD signal time sequence of the tissue structures and the BOLD signal time sequence of the cerebrospinal fluid region, obtaining a coupling coefficient and a confidence degree of each tissue structure, carrying out adaptive hierarchical aggregation based on the coupling coefficient and the confidence degree, obtaining segmented brain regions, and extracting a BOLD signal time sequence of each brain region; and carrying out cross correlation calculation based on the BOLD signal time sequence of the cerebrospinal fluid and each brain region to obtain a coupling analysis result of each brain region. The application provides a method for extracting a coupling feature of BOLD signals based on cerebrospinal fluid and multiple brain regions, and realizes quantitative coupling evaluation of brain region spatial specificity.

Description

Technical Field

[0001] This application relates to the field of medical image processing and analysis technology, and more specifically, to a method, apparatus, and device for signal coupling analysis based on cerebrospinal fluid and multiple brain regions. Background Technology

[0002] Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to study human brain activity. Recent studies have revealed a close physiological link between neural activity, hemodynamics, and cerebrospinal fluid (CSF) flow in the brain. CSF pulsation plays a crucial "lymphatic system-like" cleansing role in clearing metabolic waste products from the brain, such as amyloid protein associated with Alzheimer's disease. Therefore, accurately quantifying the coupling relationship between CSF pulsation and BOLD signals in the cerebral cortex is of significant clinical value for the early diagnosis and monitoring of neurodegenerative diseases and cognitive impairment.

[0003] Existing approaches primarily rely on whole-brain magnetic resonance imaging (MRI) data after standard preprocessing. They extract average cerebrospinal fluid (CSF) signals from the ventricular regions and perform correlation analysis with global BOLD signals. BOLD signals reflect the overall macroscopic physiological fluctuations of the brain, failing to reveal the network connections between local brain regions and their pathological local changes. This coarse-grained global signal averaging masks regional microscopic features, resulting in an unclear and ineffective understanding of the precise spatiotemporal coupling between local cortical blood oxygen dynamics and the lymphoid system (cerebrospinal fluid pulsation). Consequently, they cannot provide effective guidance for precise disease localization and pathogenesis. Summary of the Invention

[0004] This application provides a method, apparatus, and device for signal coupling analysis based on cerebrospinal fluid and multiple brain regions, which at least solves the problem of quantitative coupling assessment of local brain region spatial specificity in related technologies.

[0005] According to one aspect of the embodiments of this application, a method for signal coupling analysis based on cerebrospinal fluid and multiple brain regions is provided, including: The cerebrospinal fluid region is identified based on the acquired BOLD images, and the BOLD signal time series of the cerebrospinal fluid region is extracted. Tissue structure segmentation is performed based on the acquired T1-weighted images to obtain fine-grained tissue structures. Cross-correlation was calculated using the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence level of each tissue structure. Adaptive hierarchical aggregation was performed based on the coupling coefficient and confidence level to obtain the segmented brain regions, and the BOLD signal time series of each brain region was extracted. Cross-correlation calculations were performed on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results for each brain region.

[0006] In one embodiment, the cerebrospinal fluid region is calibrated based on the acquired BOLD image, and the BOLD signal time series of the cerebrospinal fluid region is extracted, including: Multiple BOLD images and T1-weighted images of the user to be analyzed are collected to construct a multimodal image dataset; The cerebrospinal fluid region is identified based on the BOLD images in the multimodal image dataset, and the average cerebrospinal fluid signal of the cerebrospinal fluid region at multiple time points is extracted to form the BOLD signal time series of the cerebrospinal fluid region.

[0007] In one implementation, adaptive hierarchical aggregation is performed based on the coupling coefficient and confidence level to obtain segmented brain regions, including: Step a: When the confidence level of the coupling coefficient meets the preset condition, the fine-grained tissue structure with the coupling coefficient less than the preset threshold is identified as a weakly coupled brain region; Step b: Dynamically merge the weakly coupled brain region with the spatially adjacent tissue structures that have the highest Pearson correlation in the BOLD signal time series to obtain the merged brain region; Step c: Calculate the coupling coefficient and corresponding confidence level of the merged brain regions; Step d: When the confidence level of the coupling coefficient of the merged brain region meets the preset condition and the coupling coefficient is less than the preset threshold, it is identified as a new weakly coupled brain region; when the coupling coefficient is greater than or equal to the preset threshold, it is identified as the final segmented brain region. Repeat steps b to d until the coupling coefficient of all brain regions is greater than or equal to the preset threshold to obtain the segmented brain regions.

[0008] In one implementation, cross-correlation calculations are performed based on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results for each brain region, including: Cross-correlation calculations were performed on the BOLD signal time series of the cerebrospinal fluid region and the BOLD signal time series of each brain region to obtain the cross-correlation function between cerebrospinal fluid and each brain region. Plot the cross-correlation curves for each brain region, take the peak point with the largest absolute value of the correlation coefficient on the cross-correlation curve as the coupling coefficient, and take the corresponding time as the delay time.

[0009] In one implementation, after taking the peak point with the largest absolute value of the correlation coefficient on the cross-correlation curve as the coupling coefficient and the corresponding time as the delay time, the method further includes: Based on the coupling coefficient and delay time, the lymphoid clearance efficiency index of each brain region is calculated; Based on the lymphoid clearance efficiency index of each brain region, a lymphoid clearance efficiency index map is generated.

[0010] In one implementation, the lymphoid clearance efficiency index of each brain region is calculated based on the coupling coefficient and the delay time, including: calculating according to the following formula: GCE=|C| e^(-λ t) Where GCE represents the lymphoid clearance efficiency index of the target brain region, C represents the coupling coefficient of the target brain region, t represents the delay time of the target brain region, and λ represents the attenuation coefficient of the target brain region.

[0011] In one embodiment, after extracting the BOLD signal time series of the cerebrospinal fluid region, the method further includes: Image preprocessing is performed on the BOLD image and the T1 weighted image; The image preprocessing includes one or more of time correction, motion correction, and image registration, resulting in a preprocessed BOLD image and a T1-weighted image.

[0012] According to another aspect of the embodiments of this application, a signal coupling analysis device based on cerebrospinal fluid and multiple brain regions is provided, comprising: The cerebrospinal fluid pulsation signal extraction module is used to calibrate the cerebrospinal fluid region based on the acquired BOLD image and extract the BOLD signal time series of the cerebrospinal fluid region. The tissue structure segmentation module is used to segment tissue structures based on the acquired T1-weighted images to obtain fine-grained tissue structures. The brain region aggregation and signal extraction module is used to calculate the cross-correlation between the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence of each tissue structure. Based on the coupling coefficient and confidence, adaptive hierarchical aggregation is performed to obtain the segmented brain regions, and the BOLD signal time series of each brain region is extracted. The coupling analysis module is used to perform cross-correlation calculations based on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results for each brain region.

[0013] In one implementation, it further includes: The clearance efficiency assessment module is used to calculate the lymphoid clearance efficiency index of each brain region based on the coupling coefficient and delay time; Based on the lymphoid clearance efficiency index of each brain region, a lymphoid clearance efficiency index map is generated.

[0014] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the above-described signal coupling analysis method based on cerebrospinal fluid and multiple brain regions through the computer program.

[0015] The technical solutions provided in this application embodiment may include the following beneficial effects: This application, by combining T1 structural imaging for high-precision tissue segmentation, can extract BOLD signals from multiple independent anatomical brain regions and calculate their coupling relationship with cerebrospinal fluid, achieving precise quantitative assessment of brain region spatial specificity. It can accurately distinguish the specific brain region location of pathological changes, providing an important reference for clinical diagnosis. In addition, this approach is entirely based on conventional resting-state functional magnetic resonance imaging and T1 structural imaging data, requiring no additional sequences beyond the standard scanning protocol, and does not increase the scanning time and cost for the examinee. It has significant advantages for clinical application, and is especially suitable for large-scale retrospective studies and multicenter clinical cohort studies. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of a signal coupling analysis method based on cerebrospinal fluid and multiple brain regions according to an embodiment of this application; Figure 2 This is a schematic diagram of another signal coupling analysis method based on cerebrospinal fluid and multiple brain regions according to an embodiment of this application; Figure 3 This is a schematic diagram of a signal coupling analysis device based on cerebrospinal fluid and multiple brain regions according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0017] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0018] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0019] This application provides a method for extracting cerebrospinal fluid (CSF) signals based on the coupling characteristics of BOLD signals from multiple brain regions. Addressing the problems of existing technologies, such as susceptibility to interference in ventricular region extraction, damage to pulsation signals during preprocessing, and lack of spatial specificity for brain regions, this application first uses unprocessed BOLD images as regions of interest to extract the average CSF signal. This method preserves the pure and authentic temporal characteristics of CSF pulsation to the greatest extent possible, avoiding physiological signal distortion caused by conventional preprocessing and interference from the partial volume effect of periventricular tissues.

[0020] Secondly, by combining T1 structural images, BOLD signals, and cerebrospinal fluid signals, an adaptive hierarchical aggregation algorithm based on "coupling confidence" is used to obtain brain tissue segmentation templates with appropriate granularity. This aggregation algorithm starts with ultra-fine-grained brain region segmentation, calculates the coupling relationship between the BOLD signals and cerebrospinal fluid signals of each brain region, and dynamically merges "weakly coupled" brain regions to improve the signal-to-noise ratio and coupling confidence. Under a preset statistical threshold, it achieves appropriate fine-grained partitioning, avoiding the problems of weak coupling signals, high noise, and poor reliability caused by excessively high granularity and a small number of voxels in each partition, and insufficient information due to excessively low granularity and a large number of voxels in each partition. This algorithm can extract preprocessed BOLD signals from different independent anatomical brain regions.

[0021] Finally, through cross-correlation calculations, the coupling index and delay time between signals in specific brain regions and cerebrospinal fluid pulsation are precisely quantified. Based on the coupling index and delay time, a lymphoid clearance efficiency index map is calculated.

[0022] This application makes full use of conventional fMRI scan data and can achieve specific and quantitative assessment of microcirculation and metabolite clearance efficiency in different brain regions without the need for additional sequences, providing a new high-sensitivity imaging indicator for the study of the pathological mechanisms of neurodegenerative diseases and clinical auxiliary diagnosis.

[0023] The signal coupling analysis method based on cerebrospinal fluid and multiple brain regions according to embodiments of this application will be described in detail below with reference to the accompanying drawings. Figure 1 As shown, the method mainly includes the following steps: S101 calibrates the cerebrospinal fluid region based on the acquired BOLD image and extracts the BOLD signal time series of the cerebrospinal fluid region.

[0024] In one implementation, the cerebrospinal fluid region is calibrated based on the acquired BOLD images, and the BOLD signal time series of the cerebrospinal fluid region is extracted. This includes: acquiring multiple BOLD images and T1-weighted images of the user to be analyzed to construct a multi-modal image dataset; calibrating the cerebrospinal fluid region based on the BOLD images in the multi-modal image dataset, and extracting the average cerebrospinal fluid signal of the cerebrospinal fluid region at multiple time points to constitute the BOLD signal time series of the cerebrospinal fluid region.

[0025] Specifically, firstly, multiple BOLD (Blood Oxygen Level Dependent) images and T1-weighted images of one user are collected to construct a multimodal image dataset. The BOLD images should be collected at least 160 time points, and the collection area should include the entire brain, with the lowest level of the brain below the cerebellum, including the spinal canal.

[0026] Furthermore, by manually outlining, the region of interest (ROI) of the cerebrospinal fluid (CSF) is marked on the unprocessed raw BOLD image. The average BOLD signal of all voxels in the ROC at all time points is extracted to form the time series of CSF pulsation, which is the time series of BOLD signal of the CSF region.

[0027] S102 performs tissue structure segmentation based on the acquired T1-weighted image to obtain the segmented fine-grained tissue structure.

[0028] In one embodiment, after extracting the BOLD signal time series of the cerebrospinal fluid region, the method further includes: performing image preprocessing on the BOLD image and the T1-weighted image; the image preprocessing includes one or more of time correction, motion correction, and image registration to obtain the preprocessed BOLD image and the T1-weighted image.

[0029] Specifically, time correction is performed on the BOLD image to correct the phase difference caused by the inconsistent acquisition time of different layers, motion correction is performed on the BOLD image to eliminate artifacts caused by head movement during scanning, and the motion-corrected BOLD image is registered to the high-resolution T1 image space to achieve spatial alignment between functional and structural images.

[0030] Based on the preprocessed T1-weighted image, tissue structure segmentation is performed to obtain fine-grained tissue structures.

[0031] S103 calculates the cross-correlation between the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence level of each tissue structure. Based on the coupling coefficient and confidence level, adaptive hierarchical aggregation is performed to obtain the segmented brain regions, and the BOLD signal time series of each brain region is extracted.

[0032] First, based on the segmented fine-grained tissue structure, cross-correlation is calculated using the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence level of each tissue structure.

[0033] First, low-pass filtering is applied to the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region. The BOLD signal time series of the cerebrospinal fluid region is used as the reference signal, and the BOLD signal time series of the tissue under test is used as the target signal. The Pearson correlation coefficient between the reference signal and the target signal is calculated, resulting in a cross-correlation function curve with time delay as the independent variable and correlation coefficient as the dependent variable. The peak point with the largest absolute value of the correlation coefficient on the function curve is used as the coupling coefficient. To obtain the confidence level of this coupling coefficient, a non-parametric permutation test method is typically used. The time sequence of the cerebrospinal fluid signal is randomly shuffled 1000 times (or 5000 times). After each shuffling, the cross-correlation is recalculated with the original tissue structure signal, and the maximum absolute value of the correlation coefficient is recorded. This constructs a null distribution consisting of 1000 random coupling coefficients. Finally, the one-sided tail probability of the original observed coupling coefficient in this null distribution is calculated as the P-value, which is used as a statistical significance indicator to quantify the confidence level.

[0034] Furthermore, adaptive hierarchical aggregation is performed based on the coupling coefficient and confidence level to obtain the segmented brain regions. The aggregation is performed according to the following steps: Step a: When the confidence level of the coupling coefficient meets a preset condition, fine-grained tissue structures with coupling coefficients less than a preset threshold are identified as weakly coupled brain regions. In this embodiment, the calculated coupling coefficient is first determined to be reliable. When determining whether the confidence level meets the preset condition, a confidence threshold can be used. If the confidence level meets the threshold condition, for example, if the confidence level is greater than or equal to the threshold of 0.7, the preset condition is determined to be met; if it is less than the threshold of 0.7, the preset condition is determined not to be met, and the calculated coupling coefficient is unreliable. The specific value of the confidence threshold is not limited in this embodiment.

[0035] If the confidence threshold condition is met, then it is determined whether the coupling coefficient meets the preset coupling threshold. For example, the preset coupling threshold is 0.8. This application embodiment does not limit the specific value of the threshold. When determining whether the coupling coefficient meets the threshold, if the coupling coefficient is negative, the absolute value of the coupling coefficient can be used to make the judgment.

[0036] Step b: Dynamically merge the weakly coupled brain regions with the spatially adjacent tissue structures that have the highest Pearson correlation in the BOLD signal time series to obtain the merged brain regions.

[0037] For each weakly coupled brain region, the Pearson correlation coefficient between its BOLD signal time series and the BOLD signal time series of all spatially adjacent tissue structures is calculated. The weakly coupled brain region is then merged into the adjacent tissue structure with the highest correlation coefficient to obtain the merged brain region.

[0038] Step c: Calculate the coupling coefficient and corresponding confidence level of the merged brain regions.

[0039] Based on the merged brain regions, the average BOLD signal time series is extracted, and cross-correlation calculation is performed between the average BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence level of the merged brain regions.

[0040] Step d: When the confidence level of the coupling coefficient of the merged brain region meets the preset condition and the coupling coefficient is less than the preset threshold, it is identified as a new weakly coupled brain region; when the coupling coefficient is greater than or equal to the preset threshold, it is identified as the final segmented brain region. Repeat steps b to d until the coupling coefficient of all brain regions is greater than or equal to the preset threshold, and the segmented brain regions are obtained.

[0041] The dynamic hierarchical aggregation method of this application starts with ultra-fine-grained brain regions, calculates the coupling relationship between BOLD signals and cerebrospinal fluid signals in each micro-brain region, screens out weakly coupled brain regions with coupling coefficients below a preset threshold, and merges them with spatially adjacent neighborhoods that have the highest correlation between BOLD time series. By iteratively executing this process, a brain tissue segmentation template with appropriate fine-grainedness is finally obtained. This avoids the problems of insufficient voxels, weak coupling signals, and significant noise caused by overly fine partitioning, while also preventing the defects of information loss and inability to reflect the differential characteristics of different independent anatomical brain regions caused by overly coarse partitioning. Thus, it achieves effective extraction of preprocessed BOLD signals from each independent anatomical brain region while ensuring the signal-to-noise ratio of the coupling signal and the reliability of the results.

[0042] S104 performs cross-correlation calculations based on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results for each brain region.

[0043] Cross-correlation calculations were performed on the BOLD signal time series of the cerebrospinal fluid region and the BOLD signal time series of each brain region to obtain the cross-correlation function between cerebrospinal fluid and each brain region. Cross-correlation curves for each brain region were plotted, and the peak point with the largest absolute value of the correlation coefficient on the cross-correlation curve was taken as the coupling coefficient, and the time corresponding to it was taken as the delay time.

[0044] In one implementation, the method further includes: calculating the lymphoid clearance efficiency index of each brain region based on the coupling coefficient and the delay time; and generating a lymphoid clearance efficiency index map based on the lymphoid clearance efficiency index of each brain region.

[0045] Specifically, based on the coupling coefficient and delay time, the lymphoid clearance efficiency index of each brain region is calculated, including: according to the following formula: GCE=|C| e^(-λ t) Where GCE represents the lymphoid clearance efficiency index of the target brain region, C represents the coupling coefficient of the target brain region, t represents the delay time of the target brain region, and λ represents the attenuation coefficient of the target brain region.

[0046] Based on this formula, the lymphoid clearance efficiency index of brain regions can be calculated. By traversing all brain regions, a set of lymphoid clearance efficiency indices for each region in the whole brain can be obtained. These indices are then mapped onto a standard brain template according to their spatial location, generating a lymphoid clearance efficiency index atlas. This compresses two dimensions into a single scalar atlas, simultaneously reflecting the driving force and transmission resistance of the lymphoid system. Using multi-dimensional quantification and dimensional integration methods will help clinicians more intuitively and comprehensively assess the brain's true clearance capacity.

[0047] The multi-brain region cerebrospinal fluid-BOLD coupling index and delay map extracted in this application can sensitively reflect the microcirculation efficiency of different brain regions' lymphatic system-like systems. Based on the analysis results of this application, users can obtain novel, highly sensitive quantitative imaging indicators for the early screening, disease monitoring, and efficacy evaluation of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, which has significant clinical translational value.

[0048] To facilitate understanding of the methods in the embodiments of this application, the following description is provided in conjunction with the appendix. Figure 2 Further description. For example... Figure 2 As shown, it includes: S1 selects multiple BOLD images and T1-weighted images of the user to be analyzed to establish a multi-modal image dataset; S2 delineates the region of interest for cerebrospinal fluid based on the BOLD image, and extracts the average signal of cerebrospinal fluid pulsation within the region of interest using unprocessed BOLD image data. S3 performs image preprocessing on BOLD and T1 weighted images, including temporal correction, motion correction, image registration, and high-fine-grained structural tissue segmentation. S4 utilizes segmentation templates of different anatomical structures and tissues, and uses preprocessed BOLD image data and cerebrospinal fluid pulsation signals to perform coupled confidence-adaptive hierarchical aggregation to obtain brain structural partitions of appropriate granularity, and extracts the average signal of each brain structural partition. S5 performs low-pass filtering on the average BOLD signals of cerebrospinal fluid and different brain regions obtained from steps S2 and S4, and then calculates the cross-correlation. S6 plots signal cross-correlation curves for different brain regions, extracting coupling coefficients and delay times. Based on the coupling coefficients and delay times, a lymphoid clearance efficiency index map is calculated.

[0049] This application has the following technical effects: (1) Achieving quantitative coupling assessment of brain region spatial specificity. This application combines T1 structural imaging for high-precision tissue segmentation, enabling the extraction of BOLD signals from multiple independent anatomical brain regions (including the frontal lobe, parietal lobe, temporal lobe, occipital cortex, and deep structures such as the hippocampus and basal ganglia), and calculating the coupling coefficient and delay time with cerebrospinal fluid, thereby achieving a precise quantitative assessment of brain region spatial specificity. In contrast, existing technologies only calculate a single global coupling index, completely ignoring the spatial heterogeneity of different anatomical regions of the brain in terms of microcirculation and metabolic waste clearance capacity, failing to distinguish the specific brain region location of pathological changes, and thus having relatively limited clinical diagnostic value.

[0050] (2) Simultaneously extracting parameters in two dimensions: coupling strength and time delay. This application quantifies the coupling coefficient between cerebrospinal fluid and BOLD signals in various brain regions using cross-correlation curves, and also accurately extracts the coupling time delay parameter, thereby constructing a spatiotemporal dynamic map reflecting the differences in the clearance efficiency of metabolic waste in different brain regions. This delay parameter has important physiological significance, as it can reflect the propagation speed of cerebrospinal fluid microcirculation in different brain regions, which is a key piece of information that is completely ignored by existing global correlation schemes.

[0051] (3) No additional scanning sequences are required, making full use of routine clinical functional magnetic resonance imaging data. This application is based entirely on routine resting-state functional magnetic resonance imaging and T1 structural image data, without adding any additional sequences beyond the standard scanning protocol, and without increasing the scanning time and cost for the examinee. Compared with technical solutions that require special phase contrast sequences (PC-MRI), it has significant advantages for clinical application, and is especially suitable for large-scale retrospective studies and multicenter clinical cohort studies.

[0052] (4) Provides novel, highly sensitive imaging biomarkers for neurodegenerative diseases. The multi-brain region cerebrospinal fluid-BOLD coupling index and delay map extracted in this application can sensitively reflect the microcirculation efficiency of different brain regions' lymphoid systems, providing novel, highly sensitive quantitative imaging indicators for early screening, disease monitoring, and efficacy evaluation of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease, and has important clinical translational value.

[0053] According to another aspect of the embodiments of this application, a signal coupling analysis device based on cerebrospinal fluid and multiple brain regions is also provided for implementing the above-described signal coupling analysis method based on cerebrospinal fluid and multiple brain regions. For example... Figure 3 As shown, the device includes: The cerebrospinal fluid pulsation signal extraction module 301 is used to calibrate the cerebrospinal fluid region based on the acquired BOLD image and extract the BOLD signal time series of the cerebrospinal fluid region. The tissue structure segmentation module 302 is used to segment the tissue structure based on the acquired T1-weighted image to obtain the segmented fine-grained tissue structure. The brain region aggregation and signal extraction module 303 is used to calculate the cross-correlation between the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region, to obtain the coupling coefficient and confidence of each tissue structure, to perform adaptive hierarchical aggregation based on the coupling coefficient and confidence, to obtain the segmented brain regions, and to extract the BOLD signal time series of each brain region. The coupling analysis module 304 is used to perform cross-correlation calculations based on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results of each brain region.

[0054] In one embodiment, it further includes: a clearance efficiency assessment module for calculating the lymphoid clearance efficiency index of each brain region based on the coupling coefficient and the delay time; Based on the lymphoid clearance efficiency index of each brain region, a lymphoid clearance efficiency index map is generated.

[0055] It should be noted that the above embodiments of the signal coupling analysis device based on cerebrospinal fluid and multiple brain regions, when executing the signal coupling analysis method based on cerebrospinal fluid and multiple brain regions, are only illustrative examples of the above-described functional module division. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the signal coupling analysis device based on cerebrospinal fluid and multiple brain regions provided in the above embodiments and the embodiment of the signal coupling analysis method based on cerebrospinal fluid and multiple brain regions belong to the same concept, and its implementation process is detailed in the method embodiment, which will not be repeated here.

[0056] According to another aspect of the present application, an electronic device corresponding to the signal coupling analysis method based on cerebrospinal fluid and multiple brain regions provided in the foregoing embodiments is also provided, so as to execute the above-described signal coupling analysis method based on cerebrospinal fluid and multiple brain regions.

[0057] Please refer to Figure 4 This illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 4 As shown, the electronic device includes: a processor 400, a memory 401, a bus 402, and a communication interface 403. The processor 400, the communication interface 403, and the memory 401 are connected via the bus 402. The memory 401 stores a computer program that can run on the processor 400. When the processor 400 runs the computer program, it executes the signal coupling analysis method based on cerebrospinal fluid and multiple brain regions provided in any of the foregoing embodiments of this application.

[0058] The memory 401 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 403 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network.

[0059] Bus 402 can be an ISA bus, PCI bus, or EISA bus, etc. Buses can be divided into address buses, data buses, control buses, etc. Memory 401 is used to store programs. After receiving execution instructions, processor 400 executes the program. The signal coupling analysis method based on cerebrospinal fluid and multiple brain regions disclosed in any of the aforementioned embodiments of this application can be applied to processor 400, or implemented by processor 400.

[0060] The processor 400 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 400 or by instructions in software form. The processor 400 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 401. The processor 400 reads the information in memory 401 and, in conjunction with its hardware, completes the steps of the above method.

[0061] The electronic device provided in this application embodiment and the signal coupling analysis method based on cerebrospinal fluid and multiple brain regions provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0062] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The above embodiments merely illustrate several implementation methods of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this patent should be determined by the appended claims.

Claims

1. A method for signal coupling analysis based on cerebrospinal fluid and multiple brain regions, characterized in that, include: The cerebrospinal fluid region is identified based on the acquired BOLD images, and the BOLD signal time series of the cerebrospinal fluid region is extracted. Tissue structure segmentation is performed based on the acquired T1-weighted images to obtain fine-grained tissue structures. Cross-correlation was calculated using the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence level of each tissue structure. Adaptive hierarchical aggregation was performed based on the coupling coefficient and confidence level to obtain the segmented brain regions, and the BOLD signal time series of each brain region was extracted. Cross-correlation calculations were performed on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results for each brain region.

2. The method according to claim 1, characterized in that, Based on the acquired BOLD images, cerebrospinal fluid regions are calibrated, and the BOLD signal time series of the cerebrospinal fluid regions is extracted, including: Multiple BOLD images and T1-weighted images of the user to be analyzed are collected to construct a multimodal image dataset; The cerebrospinal fluid region is identified based on the BOLD images in the multimodal image dataset, and the average cerebrospinal fluid signal of the cerebrospinal fluid region at multiple time points is extracted to form the BOLD signal time series of the cerebrospinal fluid region.

3. The method according to claim 1, characterized in that, Adaptive hierarchical aggregation is performed based on the coupling coefficient and confidence level to obtain the segmented brain regions, including: Step a: When the confidence level of the coupling coefficient meets the preset condition, the fine-grained tissue structure with the coupling coefficient less than the preset threshold is identified as a weakly coupled brain region; Step b: Dynamically merge the weakly coupled brain region with the spatially adjacent tissue structures that have the highest Pearson correlation in the BOLD signal time series to obtain the merged brain region; Step c: Calculate the coupling coefficient and corresponding confidence level of the merged brain regions; Step d: When the confidence level of the coupling coefficient of the merged brain region meets the preset condition and the coupling coefficient is less than the preset threshold, it is identified as a new weakly coupled brain region; when the coupling coefficient is greater than or equal to the preset threshold, it is identified as the final segmented brain region. Repeat steps b to d until the coupling coefficient of all brain regions is greater than or equal to the preset threshold to obtain the segmented brain regions.

4. The method according to claim 1, characterized in that, Based on cross-correlation calculations of BOLD signal time series from cerebrospinal fluid and various brain regions, coupling analysis results for each brain region were obtained, including: Cross-correlation calculations were performed on the BOLD signal time series of the cerebrospinal fluid region and the BOLD signal time series of each brain region to obtain the cross-correlation function between cerebrospinal fluid and each brain region. Plot the cross-correlation curves for each brain region, take the peak point with the largest absolute value of the correlation coefficient on the cross-correlation curve as the coupling coefficient, and take the corresponding time as the delay time.

5. The method according to claim 4, characterized in that, After taking the peak point with the largest absolute value of the correlation coefficient on the cross-correlation curve as the coupling coefficient and its corresponding time as the delay time, the following steps are also included: Based on the coupling coefficient and delay time, the lymphoid clearance efficiency index of each brain region is calculated; Based on the lymphoid clearance efficiency index of each brain region, a lymphoid clearance efficiency index map is generated.

6. The method according to claim 5, characterized in that, Based on the coupling coefficient and delay time, the lymphoid clearance efficiency index of each brain region is calculated, including: according to the following formula: GCE=|C| e^(−λ t) Where GCE represents the lymphoid clearance efficiency index of the target brain region, C represents the coupling coefficient of the target brain region, t represents the delay time of the target brain region, and λ represents the attenuation coefficient of the target brain region.

7. The method according to claim 1, characterized in that, After extracting the BOLD signal time series of the cerebrospinal fluid region, the method further includes: Image preprocessing is performed on the BOLD image and the T1 weighted image; The image preprocessing includes one or more of time correction, motion correction, and image registration, resulting in a preprocessed BOLD image and a T1-weighted image.

8. A signal coupling analysis device based on cerebrospinal fluid and multiple brain regions, characterized in that, include: The cerebrospinal fluid pulsation signal extraction module is used to calibrate the cerebrospinal fluid region based on the acquired BOLD image and extract the BOLD signal time series of the cerebrospinal fluid region. The tissue structure segmentation module is used to segment tissue structures based on the acquired T1-weighted images to obtain fine-grained tissue structures. The brain region aggregation and signal extraction module is used to calculate the cross-correlation between the BOLD signal time series of each tissue structure and the BOLD signal time series of the cerebrospinal fluid region to obtain the coupling coefficient and confidence of each tissue structure. Based on the coupling coefficient and confidence, adaptive hierarchical aggregation is performed to obtain the segmented brain regions, and the BOLD signal time series of each brain region is extracted. The coupling analysis module is used to perform cross-correlation calculations based on the time series of BOLD signals from cerebrospinal fluid and various brain regions to obtain the coupling analysis results for each brain region.

9. The apparatus according to claim 8, characterized in that, Also includes: The clearance efficiency assessment module is used to calculate the lymphoid clearance efficiency index of each brain region based on the coupling coefficient and delay time; Based on the lymphoid clearance efficiency index of each brain region, a lymphoid clearance efficiency index map is generated.

10. An electronic device, characterized in that, It includes a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the signal coupling analysis method based on cerebrospinal fluid and multiple brain regions as described in any one of claims 1 to 7.

Images