Method and device for measuring coupling of cerebrospinal fluid event level flow rate with brain regions and brain networks

Abstract

The application discloses a cerebrospinal fluid event-level flow rate and brain region and brain network coupling measurement method and device, relates to the magnetic resonance imaging technical field, and the method comprises the following steps: based on the magnetic resonance data obtained by collecting simultaneously covering the lowermost cerebrospinal fluid, adjacent upper cerebrospinal fluid and brain region in less than or equal to the propagation delay time between two layers of cerebrospinal fluid, determining the candidate inflow event in the lowermost cerebrospinal fluid, and calculating the apparent flow rate of the candidate inflow event based on the positive delay propagation confirmed in the adjacent upper cerebrospinal fluid based on the candidate inflow event; based on the occurrence time of the candidate inflow event, the signals of different brain regions or brain networks in the magnetic resonance data are aligned, and the coupling features of the candidate inflow event are extracted from the aligned functional magnetic resonance data; the apparent flow rate and coupling features of the candidate inflow event are used as measurement results. Thus, the cerebrospinal fluid flow rate quantitative measurement and the coupling feature extraction of brain regions and brain networks are simultaneously completed on the precision of a single physiological event.

Description

Technical Field

[0001] This application relates to the field of magnetic resonance imaging technology, and in particular to a method and device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. Background Technology

[0002] Measuring cerebrospinal fluid flow velocity and its coupling relationship with brain region activity is crucial for understanding the clearance of metabolic waste from brain tissue and neurovascular coupling.

[0003] Existing whole-brain gray matter mean oxygenation level-dependent (gBOLD)-cerebrospinal fluid (CSF) coupling analysis based on functional magnetic resonance imaging (fMRI) data typically performs correlation analysis between the entire gBOLD time series and the CSF time series at the skull base. While this method can reflect the overall coupling relationship, it cannot obtain interlayer propagation delay and apparent flow velocity at the scale of a single CSF inflow event, nor can it distinguish the coupling differences between different brain regions or brain networks under different event phenotypes. Furthermore, the CSF signal at the skull base is easily affected by head movements and boundary volume effects, and existing overall correlation analyses lack motion quality markers and sensitivity tests for individual events.

[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this application is to provide a method and device for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. This aims to solve the technical problems of existing functional magnetic resonance gBOLD-cerebrospinal fluid overall coupling analysis, which cannot measure the interlayer apparent propagation velocity of cerebrospinal fluid signals at the single event scale, has difficulty in extracting differential coupling features of brain regions or brain networks, and has difficulty in performing event-level control due to mixed motion.

[0006] To achieve the above objectives, this application proposes a method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks, the method comprising: Acquire functional magnetic resonance imaging (fMRI) data to be processed. The fMRI data is acquired with a repetition time of no more than 1.0 second and simultaneously covers the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and at least one brain region in the same scan. Based on the functional magnetic resonance imaging (fMRI) data, candidate inflow events in the lowest layer of cerebrospinal fluid are identified; the positive delayed propagation of the candidate inflow events is confirmed in the adjacent upper layer of cerebrospinal fluid to obtain the candidate propagation delay; the apparent velocity of the candidate inflow events is calculated based on the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid and the candidate propagation delay. Based on the occurrence time of the candidate inflow events, the signals of different brain regions or brain networks in the functional magnetic resonance imaging (fMRI) data are aligned, and the coupling features of the candidate inflow events are extracted from the aligned fMRI data. The apparent flow rate and coupling characteristics of the candidate inflow events are used as measurement results.

[0007] In one embodiment, the step of confirming the positive delayed propagation of the candidate inflow event in the adjacent upper cerebrospinal fluid and obtaining the candidate propagation delay includes: Obtain the motion parameter sequence corresponding to the functional magnetic resonance data, and calculate the signal change sequence based on the functional magnetic resonance data; Based on the motion parameter sequence and the signal change sequence, the degree of motion-related signal anomaly within the peak time and neighboring time window of the candidate inflow event is evaluated to obtain the evaluation result. Based on the evaluation results, quality labels are added to the candidate inflow events; The quality label indicates a target inflow event that meets a preset motion quality standard, and based on the target inflow event, positive delayed propagation is confirmed in the adjacent upper cerebrospinal fluid to obtain candidate propagation delay.

[0008] In one embodiment, the step of evaluating the degree of motion-related signal anomaly within the peak time and neighborhood time window of the candidate inflow event based on the motion parameter sequence and the signal change sequence, and obtaining the evaluation result, includes: Based on the motion parameter sequence and the signal change sequence, the framewise displacement (FD) corresponding to the peak moment of the candidate inflow event is calculated, and the signal change (DVARS) corresponding to the peak moment is calculated based on the functional magnetic resonance data. When the frame-by-frame displacement FD exceeds a preset displacement threshold or the signal change DVARS exceeds a preset change threshold, the corresponding candidate inflow event is marked as not meeting the preset motion quality standard. Calculate the maximum value of the frame-by-frame displacement FD and the maximum value of the signal change DVARS of the candidate inflow event within a preset time window; When the maximum value of the frame-by-frame displacement FD exceeds the preset displacement threshold or the maximum value of the signal change DVARS exceeds the preset change threshold, the corresponding candidate inflow event is marked as not meeting the preset motion quality standard.

[0009] In one embodiment, the step of evaluating the degree of motion-related signal anomaly within the peak time and neighborhood time window of the candidate inflow event based on the motion parameter sequence and the signal change sequence, and obtaining the evaluation result, further includes: A motion regression model is constructed based on the motion parameter sequence, and the functional magnetic resonance imaging data is signal corrected according to the motion regression model. Based on the signal change sequence, candidate inflow events in the lowest layer of cerebrospinal fluid are re-determined in the corrected functional magnetic resonance imaging data; The candidate inflow events before and after correction are matched according to the tolerance of peak time and neighborhood time window to determine the retention status of each candidate inflow event before correction, and the event retention rate before and after correction is calculated. Based on the event retention rate, unmatched candidate inflow events are marked as events with motion-related signal anomalies, and the event retention rate and the degree of motion-related signal anomalies are used as the evaluation results.

[0010] In one embodiment, the step of extracting coupling features of the candidate inflow event from aligned functional magnetic resonance data includes: Obtain the peak value and apparent flow rate of each candidate inflow event; Based on the apparent flow rate and the peak value, each candidate inflow event is classified into a different event phenotype; Using the peak moment of the candidate inflow event as the zero point, the signal features of each brain region or brain network within a preset time window are extracted from the functional magnetic resonance data. The signal features are grouped according to the event phenotype to obtain the signal feature grouping corresponding to each event phenotype; The signal features among the various signal feature groups are compared, and the comparison results are used as the coupling features of the candidate inflow events.

[0011] In one embodiment, the step of obtaining the peak magnitude of each of the candidate inflow events includes: Extract the signal value of the lowest layer of cerebrospinal fluid corresponding to the peak time of the candidate inflow event, and use the signal value as the original peak height; Extract the baseline signal value of the lowest layer of cerebrospinal fluid within a preset time window including the peak moment of the candidate inflow event; Calculate the difference between the original peak height and the signal baseline value, and use the difference as the peak significance of the candidate inflow event; Obtain the peak saliency set of all candidate inflow events within the collection operation to which the candidate inflow event belongs; Based on the cumulative distribution position of the peak significance in the set of peak significance, the peak significance is converted into a percentile value within the acquisition run, and the percentile value is used as the normalized peak value within the run of the candidate inflow event.

[0012] In one embodiment, the step of confirming the positive delayed propagation of the candidate inflow event in the adjacent upper cerebrospinal fluid to obtain the candidate propagation delay; and calculating the apparent velocity of the candidate inflow event based on the actual interlayer distance between the lowest cerebrospinal fluid layer and the adjacent upper cerebrospinal fluid layer and the candidate propagation delay, further includes: Based on the time series of the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid after layer-time correction, positive delayed cross-correlation is calculated within the local event window of the candidate inflow event, and the time offset when the correlation value of the positive delayed cross-correlation reaches its maximum is obtained. The time offsets greater than 0 are used as candidate propagation delays, and it is determined whether the candidate propagation delays are less than or equal to a preset maximum delay threshold. When the candidate propagation delay is less than or equal to the maximum delay threshold, check whether there is a local peak signal in the adjacent upper cerebrospinal fluid within a preset time range after the peak time of the candidate inflow event; If the local peak signal exists and the maximum correlation value is greater than the preset correlation coefficient threshold, then it is confirmed that the candidate inflow event has positive delayed propagation in the adjacent upper cerebrospinal fluid. According to the formula Calculate the apparent flow rate of the candidate inflow event, where, This refers to the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid along the normal direction of the fluid layer. For the candidate propagation delay, Let be the apparent flow rate of the i-th propagation confirmation event.

[0013] Furthermore, to achieve the above objectives, this application also proposes a coupling measurement device for cerebrospinal fluid event-level flow velocity with brain regions and brain networks, the coupling measurement device for cerebrospinal fluid event-level flow velocity with brain regions and brain networks comprising: The acquisition module is used to acquire functional magnetic resonance imaging (fMRI) data to be processed. The fMRI data is acquired with a repetition time of no more than 1.0 second and simultaneously covers the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and at least one brain region in the same scan. The calculation module is used to determine candidate inflow events in the lowest layer of cerebrospinal fluid based on the functional magnetic resonance imaging data; confirm the positive delayed propagation of the candidate inflow events in the adjacent upper layer of cerebrospinal fluid to obtain the candidate propagation delay; and calculate the apparent flow velocity of the candidate inflow events based on the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid and the candidate propagation delay. The alignment module is used to align signals from different brain regions or brain networks in the functional magnetic resonance imaging (fMRI) data based on the occurrence time of the candidate inflow events, and to extract the coupling features of the candidate inflow events from the aligned fMRI data.

[0014] Furthermore, to achieve the above objectives, this application also proposes a device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the coupling measurement method for cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the steps of the above-described method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: This application utilizes functional magnetic resonance imaging (fMRI) data with a repetition time (TR) of no more than 1.0 second, covering the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and the whole brain in the same scan, to obtain interlayer propagation delay and apparent flow velocity at the scale of a single candidate inflow event; extracts event-locked coupling features by aligning signals from different brain regions or brain networks with the event peak time; and reduces the impact of motion confounding on the interpretation of results through event-level motion quality labeling and multi-model sensitivity analysis. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0019] Figure 1This is a flowchart illustrating an embodiment of the method for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in this application. Figure 2 This is a schematic diagram illustrating the overall technical process of the coupling measurement method of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in this application. Figure 3 This is a flowchart illustrating Embodiment 2 of the method for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in this application. Figure 4 A schematic diagram of the head motion quality control framework consisting of event timing checks, re-detection of peaks after motion cleanup, and result interpretation provided for this application; Figure 5 This is a flowchart illustrating Embodiment 3 of the method for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in this application. Figure 6 This application illustrates the DMN signal rise from -12 to -8 seconds before a rapidly propagating event and the direction of the effect under different motion cleanup models. Figure 7 This is a schematic diagram illustrating the definition of the event level of the peak size of the lowest layer of cerebrospinal fluid using the coupling measurement method of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in this application. Figure 8 This is a schematic diagram of the module structure of the coupling measurement device for cerebrospinal fluid event-level flow velocity with brain regions and brain networks according to an embodiment of this application; Figure 9 This is a schematic diagram of the hardware operating environment of the coupling measurement method between cerebrospinal fluid event-level flow velocity and brain regions and brain networks in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or magnetic resonance platform capable of performing the above functions. The following description uses a magnetic resonance platform as an example to illustrate this embodiment and the subsequent embodiments.

[0024] Based on this, embodiments of this application provide a method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks according to this application.

[0025] In this embodiment, the method for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks includes steps S10 to S40: Step S10: Obtain the magnetic resonance data to be processed. The functional magnetic resonance data is acquired within a propagation delay time less than or equal to that between two layers of cerebrospinal fluid, and simultaneously covers the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and the brain region. It should be noted that the magnetic resonance data is functional magnetic resonance imaging (fMRI) data of the brain acquired through a magnetic resonance imaging (MRI) device. The repetition time (TR) is the time interval between two adjacent volume samples; in this embodiment, TR is no greater than 1.0 second. The lowest layer of cerebrospinal fluid (CSF) is the CSF layer at the base of the skull where inflow-related signal enhancement can be detected; the adjacent upper layer of CSF is the CSF layer located above it along the normal direction of the slice. Brain regions are gray matter areas defined according to anatomical or functional standards; brain networks are sets of brain regions defined according to functional connectivity or standard atlases, including but not limited to the default mode network, dorsal attention network, and sensorimotor network.

[0026] It is understandable that setting the repetition time (TR) of functional magnetic resonance imaging (fMRI) data to no more than 1.0 second, and simultaneously covering the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and the whole brain region in the same scan, can improve the temporal resolution of interlayer propagation delay and enable the apparent flow velocity and brain region or brain network coupling characteristics of a single candidate inflow event to be calculated based on the same set of data.

[0027] Understandably, when the regular repetition time is close to or exceeds the propagation delay of the target to be distinguished, the cross-correlation peak can only fall on a small number of discrete time points, making it difficult to distinguish between fast and slow propagation events. Therefore, using ultra-short repetition time acquisition can improve the estimation accuracy of inter-layer propagation delay.

[0028] In the specific implementation, refer to Figure 2The steps of ultra-short TR acquisition and adjacent layer signal acquisition in the (Ultra-short TR functional magnetic resonance imaging two-layer cerebrospinal fluid flow velocity measurement procedure) are as follows: data acquisition is performed using a rapid whole-brain gradient echo planar imaging sequence, with a repetition time set to no more than 1.0 second, preferably 0.4-0.75 seconds, covering the whole brain and skull base CSF (cerebrospinal fluid); the layer thickness is set to 1 mm to 3 mm, and the scanning range extends from the lowest layer of cerebrospinal fluid z1 at the skull base to the cerebral cortex region, with the intermediate layer covering key anatomical structures such as the cerebral aqueduct, to ensure that the whole brain gray matter oxygenation level-dependent signal and the two layers of cerebrospinal fluid signals are obtained simultaneously, that is, the signals of the lowest layer of cerebrospinal fluid z1 and the adjacent upper layer of cerebrospinal fluid z2. The scanning duration is set to no less than 5 minutes to ensure that a sufficient number of candidate inflow events are obtained. Six-degree-of-freedom head motion parameters are acquired simultaneously as necessary auxiliary data, and physiological signals such as respiration and heart rate are used as preferred auxiliary data (magnetic resonance data).

[0029] Step S20: Confirm the positive delayed propagation of the candidate inflow event in the adjacent upper cerebrospinal fluid to obtain the candidate propagation delay; calculate the apparent flow velocity of the candidate inflow event based on the actual interlayer distance between the lowest cerebrospinal fluid and the adjacent upper cerebrospinal fluid and the candidate propagation delay. It should be noted that candidate inflow events are local positive signal peaks detected in the time series of the lowest layer of cerebrospinal fluid, reflecting inflow-related enhancement of the magnetic resonance imaging (MRI) signal at that location. Positive delayed propagation is defined as the signal response of the adjacent upper layer of cerebrospinal fluid being later in time than the signal peak of the lowest layer, with the delay direction fixed as propagation from the lowest layer to the adjacent upper layer. Apparent flow velocity is the apparent propagation speed of the cerebrospinal fluid signal from the lowest layer to the adjacent upper layer, calculated by dividing the actual interlayer distance by the propagation delay time.

[0030] Understandably, by taking each candidate inflow event as an independent detection unit and confirming whether the corresponding candidate inflow event has detectable upward propagation and calculating its respective propagation speed, that is, by taking the event as an anchor point to confirm its positive delayed propagation in the adjacent upper cerebrospinal fluid, the apparent flow velocity measurement between cerebrospinal fluid signal layers at the level of a single physiological event is realized.

[0031] Understandably, since the amplitude of the lowest layer of cerebrospinal fluid peak reflects the intensity of the inflow-related signal, in order to avoid the influence of various factors such as inflow enhancement effect, partial volume effect, layer profile, global physiological fluctuations and reconstruction algorithm on this signal intensity, the flow velocity cannot be inferred solely from the peak height. It is necessary to measure the amplitude of the inflow-related signal and the propagation speed as two independent dimensions to avoid mistaking the peak intensity for the flow velocity or absolute flow rate, thereby ensuring the accuracy and reliability of the flow velocity measurement.

[0032] In the specific implementation, refer to Figure 2The underlying peak events and propagation velocity steps in functional magnetic resonance imaging (fMRI) data are used to perform steady-state volume removal, slice time-series correction, rigid head motion estimation, and low-frequency drift correction. A region of interest (ROI) z1 is determined in the lowest layer of intracranial cerebrospinal fluid space, and a region of interest z2 is determined in the adjacent upper layer along the slice normal direction. Local positive peaks are detected in the z1 time series, and local event windows are established. Positive time delay cross-correlation is calculated based on the z1 and z2 time series corrected for actual slice acquisition times, and the positive time offset that maximizes the correlation value is denoted as the candidate propagation delay τ. After confirming the existence of a local peak in z2 near the corresponding time and that the maximum correlation value reaches a preset threshold, the event-level apparent velocity is calculated according to v_app = Δz / τ, where Δz is the actual center-to-center distance between the two ROIs along the slice normal direction.

[0033] Optionally, step S20 further includes: Based on the time series of the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid after layer-time correction, positive delayed cross-correlation is calculated within the local event window of the candidate inflow event, and the time offset when the correlation value of the positive delayed cross-correlation reaches its maximum is obtained. The time offsets greater than 0 are used as candidate propagation delays, and it is determined whether the candidate propagation delays are less than or equal to a preset maximum delay threshold. When the candidate propagation delay is less than or equal to the maximum delay threshold, check whether there is a local peak signal in the adjacent upper cerebrospinal fluid within a preset time range after the peak time of the candidate inflow event; If the local peak signal exists and the maximum correlation value is greater than the preset correlation coefficient threshold, then it is confirmed that the candidate inflow event has positive delayed propagation in the adjacent upper cerebrospinal fluid. According to the formula Calculate the apparent flow rate of the candidate inflow event, where, This refers to the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid along the normal direction of the fluid layer. For the candidate propagation delay, Let be the apparent flow rate of the i-th propagation confirmation event.

[0034] It should be noted that positive delay cross-correlation calculates the correlation between the time series of the lowest cerebrospinal fluid layer and the time series of the adjacent upper cerebrospinal fluid layer along the positive time offset direction. The time offset is the positive time offset value that maximizes the cross-correlation value. The candidate propagation delay is the time difference between the peak time of the lowest cerebrospinal fluid layer and the response time of the adjacent upper cerebrospinal fluid layer. The maximum delay threshold is a pre-set upper limit for the allowable propagation delay. The local peak signal is the local peak value of the signal appearing in the adjacent upper cerebrospinal fluid layer near the candidate propagation delay time. The correlation coefficient threshold is a pre-set minimum cross-correlation value used to determine whether positive delay propagation exists. The actual interlayer distance is the geometric distance between the lowest cerebrospinal fluid layer and the adjacent upper cerebrospinal fluid layer measured along the layer normal.

[0035] Understandably, the actual inter-slice distance can be calculated along the slice normal direction based on image head information or reconstructed coordinates. Apparent flow velocity reflects the apparent speed at which cerebrospinal fluid magnetic resonance signals propagate from the lowest slice to the adjacent upper slice, and is not equivalent to the absolute physical flow velocity or volumetric flow rate that has not been independently calibrated.

[0036] Step S30: Based on the occurrence time of the candidate inflow event, the signals of different brain regions or brain networks in the functional magnetic resonance data are aligned, and the coupling features of the candidate inflow event are extracted from the aligned functional magnetic resonance data. It should be noted that the occurrence time is the moment when the candidate inflow event reaches a local peak in the lowest layer of the cerebrospinal fluid time series. Alignment uses the occurrence time of the candidate inflow event as the time zero point, repositioning the blood oxygenation level-dependent signal time series of each brain region or brain network on the time axis so that the occurrence times of all events coincide at the same time origin. Brain networks are collections of brain regions with functional connectivity, including but not limited to default mode networks, dorsal attention networks, and sensorimotor networks. Coupling characteristics are quantitative indicators reflecting the correlation between brain region or brain network signals and cerebrospinal fluid inflow events.

[0037] It is understandable that by aligning the signals of different brain regions or brain networks with the occurrence time of candidate inflow events as the time anchor, each candidate inflow event can have its own independent time reference system, enabling the signals of different brain regions or brain networks to be accurately aligned with the occurrence time of the event as the benchmark. This allows for the extraction of the brain region response pattern corresponding to each event, revealing the regional and network coupling characteristics that cannot be distinguished by the entire correlation analysis.

[0038] Understandably, by treating each refined cerebrospinal fluid event as an independent analysis unit, any anatomical brain region or functional network time series in the same whole-brain functional magnetic resonance imaging can be locked to the same event, and the intrabrain coupling characteristics of different event phenotypes can be compared. This provides a data structure basis for subsequent grouping and comparison of event phenotypes such as peak size and propagation speed, making it possible to analyze the differential coupling of brain regions or brain networks under different phenotypes.

[0039] In the specific implementation, refer to Figure 2 In the motion quality control steps, after calculating the apparent velocity of each candidate inflow event, the frame-by-frame displacement (FD) at the peak time and the DVARS (Difference in Radiation and Variation of Responses) reflecting the amplitude of whole-brain signal changes at adjacent time points are temporally matched with the candidate inflow events within the single point of the peak time and the surrounding time window. Events with abnormal motion-related signals are marked, eliminated, or downweighted according to the indicators, and event quality labels are output. These quality labels are then associated and stored with the peak time, propagation delay, and apparent velocity of the corresponding events.

[0040] Then, taking the peak time of each candidate inflow event as the zero point, signal features within a preset time window are extracted from the blood oxygenation level-dependent signal time series of each brain region or brain network. These extracted signal features are then used to form a coupled feature dataset indexed by events and categorized by brain region or network. The preset time window includes a pre-event window, a mid-event window, and a post-event window. The specific window position and length can be set according to the analysis requirements; for example, the signal mean within the window from 12 seconds to 8 seconds before the event can be extracted. Signal features include, but are not limited to, the signal mean within the window, the signal slope, the signal integral, or the signal difference between networks.

[0041] Step S40: The apparent flow rate and coupling characteristics of the candidate inflow event are used as the measurement results.

[0042] It should be noted that the measurement results are the final output of all quantitative indicators related to the cerebrospinal fluid inflow event.

[0043] It is understandable that by outputting the apparent flow velocity of each candidate inflow event and its corresponding brain region or brain network coupling features as the measurement result, the measurement of the apparent flow velocity between cerebrospinal fluid signal layers and the extraction of brain region and brain network coupling features can be completed simultaneously with the accuracy of a single physiological event.

[0044] In practical implementation, measurement results can be organized and output at six levels: acquisition level, operational level, event level, motor level, brain region network level, and individual level. Acquisition level output includes repetition time, slice thickness, inter-slice spacing, slice normal, and coverage area, used to determine whether flow velocity measurement conditions are met. Operational level output includes the number of voxels in the region of interest, number of peaks, propagation confirmation rate, and distribution of motion parameters, used for quality control and protocol comparison. Event level output includes peak time, peak height, peak significance, propagation delay, maximum correlation coefficient, inter-slice distance, and apparent flow velocity, serving as core inflow correlation and flow velocity characteristics. Motor level output includes peak time motion displacement, signal change, its neighborhood maximum value, and quality label, used to record head movement effects. Brain region network level output includes the signal mean, slope, integral, and inter-network difference within the event locking window, serving as region and network coupling characteristics. Individual level output includes event rate, median velocity, and proportion of fast events, used for cohort statistics and independence testing.

[0045] This embodiment uses functional magnetic resonance imaging (fMRI) data with a repetition time (TR) of no more than 1.0 second and covering two layers of cerebrospinal fluid and the whole brain in the same scan to measure the apparent propagation velocity between layers at the single event scale; at the same time, it extracts brain region or brain network coupling features using the event peak moment as the time anchor point and outputs acquisition-level, event-level, motor-level and brain region network-level indicators.

[0046] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Step S20 also includes steps S01 to S04: Step S01: Obtain the motion parameter sequence corresponding to the functional magnetic resonance data, and calculate the signal change sequence based on the functional magnetic resonance data; Step S02: Based on the motion parameter sequence and the signal change sequence, evaluate the degree of motion-related signal anomaly within the peak time and neighborhood time window of the candidate inflow event, and obtain the evaluation result; Step S03: Add quality labels to the candidate inflow events based on the evaluation results; Step S04: Select the target inflow event indicated by the quality label as meeting the preset motion quality standard, and confirm positive delayed propagation in the adjacent upper cerebrospinal fluid based on the target inflow event to obtain candidate propagation delay.

[0047] It should be noted that the motion parameter sequence is a sequence of head rigid body motion parameters that change over time, recorded synchronously during magnetic resonance imaging (MRI) data acquisition; this includes, but is not limited to, displacements along the three translational axes and three rotational axes. The peak moment is the moment when a candidate inflow event reaches a local peak in the lowest layer of the cerebrospinal fluid time series. The neighborhood time window is the time interval extending forward and backward from the peak moment. The degree of motion contamination is the severity of interference caused by head movement to the cerebrospinal fluid signal within the candidate inflow event's time and its neighborhood time window. The quality label is information used to identify whether a candidate inflow event is contaminated by motion.

[0048] Understandably, the lowest layer of cerebrospinal fluid is located at the boundary of brain tissue and the edge of imaging coverage. Head movement may cause intrusion into the boundary of the region of interest, abrupt changes in signal intensity, and spurious peaks. Therefore, before calculating the apparent velocity, the degree of motion-related signal anomalies of each candidate inflow event within the peak time and neighborhood time window is evaluated based on motion parameters and signal changes, and a quality label is added. Only events that meet the preset motion quality criteria are included in the primary propagation confirmation and apparent velocity analysis to reduce the impact of head movement on the measurement results.

[0049] Understandably, linking the motion contamination determination results with the subsequent flow rate calculation process through quality labels allows events with substandard motion quality to be excluded from flow rate calculation. This avoids failing to trace the impact on individual events after performing motion regression at the whole sequence level, thus preserving the ability to analyze motion sensitively. It also avoids indiscriminately removing all events suspected of being affected by motion, thereby achieving a balance between data quality and event retention rate.

[0050] Furthermore, step S02 also includes: Based on the motion parameter sequence and the signal change sequence, the frame-by-frame displacement FD corresponding to the peak moment of the candidate inflow event is calculated, and the signal change DVARS corresponding to the peak moment is calculated based on the functional magnetic resonance data. When the frame-by-frame displacement FD exceeds a preset displacement threshold or the signal change DVARS exceeds a preset change threshold, the corresponding candidate inflow event is marked as not meeting the preset motion quality standard. Calculate the maximum value of the frame-by-frame displacement FD and the maximum value of the signal change DVARS of the candidate inflow event within a preset time window; When the maximum value of the frame-by-frame displacement FD exceeds the preset displacement threshold or the maximum value of the signal change DVARS exceeds the preset change threshold, the corresponding candidate inflow event is marked as not meeting the preset motion quality standard.

[0051] It should be noted that the motion displacement is a quantitative indicator reflecting the amplitude of rigid head motion, calculated based on the motion parameter sequence. The signal change is a quantitative indicator reflecting the degree of signal abrupt change, calculated based on the time derivative of the magnetic resonance signal intensity. The preset displacement threshold is a pre-set upper limit value used to determine whether the motion displacement constitutes significant motion contamination. The preset time window is a preset time interval centered on the peak moment. The maximum motion value is the maximum value reached by the motion displacement of the candidate inflow event within the preset time window. The maximum signal value is the maximum value reached by the signal change of the candidate inflow event within the preset time window.

[0052] Understandably, since single-point assessment can identify motion spikes precisely aligned with the peak moment, and neighborhood window assessment can identify the impact of motion events on signal quality within the time period before and after the peak moment, by assessing the degree of motion contamination at both the single-point level of the peak moment and the neighborhood time window level, we can capture both the direct impact of instantaneous motion at the peak moment on the signal and the signal contamination that may be caused by the accumulation of motion in the time period surrounding the peak moment, thus forming a comprehensive assessment of motion contamination.

[0053] Understandably, since motion displacement reflects the actual displacement of the head and signal change reflects the degree of signal disturbance, by incorporating these two complementary indicators into the evaluation system, the degree of motion contamination can be reflected from the perspectives of physical motion amplitude and signal quality change, respectively. This avoids the one-sidedness of a single indicator and thus more accurately identifies motion events that truly affect the quality of cerebrospinal fluid signals.

[0054] Optionally, step S02 further includes: A motion regression model is constructed based on the motion parameter sequence, and the functional magnetic resonance imaging data is signal corrected according to the motion regression model. Based on the signal change sequence, candidate inflow events in the lowest layer of cerebrospinal fluid are re-determined in the corrected functional magnetic resonance imaging data; The candidate inflow events before and after correction are matched according to the tolerance of peak time and neighborhood time window to determine the retention status of each candidate inflow event before correction, and the event retention rate before and after correction is calculated. Based on the event retention rate, unmatched candidate inflow events are marked as events with motion-related signal anomalies, and the event retention rate and the degree of motion-related signal anomalies are used as the evaluation results.

[0055] It should be noted that the motion regression model is a mathematical model used to regress and remove head motion-related variance from magnetic resonance imaging (MRI) signals. Signal correction involves removing motion-related signal components from the MRI data based on the motion regression model. The event retention rate is the proportion of events that are still identified as candidate inflow events after correction out of the total number of candidate inflow events before correction.

[0056] It is understandable that since peaks not retained after motion cleanup cannot be directly named motion artifacts, and regression may remove the variance shared by motion and real physiology, introducing multiple motion regression models for signal correction and comparing the event retention rates under different models can make the conclusions sensitive or robust to motion models, avoiding simply attributing unretained events to motion artifacts.

[0057] In the specific implementation, refer to Figure 4 (Bottom-level CSF peak and head motion quality control in two-layer propagation) Head motion quality control is executed sequentially in three layers: event timing inspection, event-level processing, and output and interpretation. Specifically, in the first-layer event timing inspection, for each candidate inflow event, the motion displacement FD (frame-by-frame displacement) and DVARS (signal change) at its peak moment are calculated. The maximum values ​​of motion displacement and signal change within a ±2 second time window around the peak moment are calculated. A zero distribution is generated by cyclic shifting within the same acquisition run. The maximum values ​​of the motion displacement and signal change at the single point of the peak moment and in the neighboring time window are corrected for multiple indicators. It is determined whether the motion indicators at the peak moment are in an abnormal position in the zero distribution of cyclic shifting within the run, so as to evaluate whether the peak is specifically aligned with the motion spike.

[0058] In the second-level event processing, abnormal events are marked, removed, or downweighted based on whether FD and DVARS exceed preset thresholds. Simultaneously, sensitivity analysis of the stepwise extended motion regression model is performed to observe changes in event retention rates. Candidate inflow event detection and positive delay propagation confirmation are re-executed on the corrected data. Candidate inflow events before and after correction are matched according to peak time tolerance, and the retention rates of candidate inflow events and propagation confirmation events are calculated.

[0059] In the third layer of output and interpretation, a quality label is output for each candidate inflow event, including whether the motion index of the peak time and neighborhood window exceeds a threshold, whether it is in an abnormal position in the zero distribution within the run, and whether it is sensitive to the motion regression model. The quality index is then tested to see if it can predict the confirmation status of two-layer propagation or distinguish between fast-propagating and slow-propagating events. Peaks not retained after motion cleanup are not directly identified as motion artifacts, but rather described as being sensitive to the corresponding motion model; events can be screened based on the quality label or the quality index can be included as a covariate in the statistical model.

[0060] Based on the first and second embodiments of this application, the same or similar content as the above embodiments in the third embodiment of this application can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 Step S30 also includes steps S1 to S5: Step S1: Obtain the peak value and apparent flow rate of each candidate inflow event; Step S2: Based on the apparent flow rate and the peak value, classify each candidate inflow event into different event phenotypes; Step S3: Take the peak moment of the candidate inflow event as the zero point, and extract the signal features of each brain region or brain network within a preset time window from the functional magnetic resonance data. Step S4: Group the signal features according to the event phenotype to obtain the signal feature group corresponding to each event phenotype; Step S5: Compare the signal features among the signal feature groups and use the comparison results as the coupling features of the candidate inflow events.

[0061] It should be noted that peak significance characterizes the signal prominence of a candidate inflow event relative to the local baseline; peak size is the percentile position of peak significance among all candidate inflow events within the acquisition run. Event phenotype is an event category classified based on attributes such as the standardized peak size and apparent flow velocity. The preset time window is a signal extraction interval set based on the event peak time, and may include a pre-event window, an in-event window, and a post-event window. Signal features are quantitative indicators extracted from blood oxygenation level-dependent signals of brain regions or brain networks within the preset time window.

[0062] Understandably, by acquiring the two independent dimensions of peak size and apparent velocity of each candidate inflow event, and classifying the events into different event phenotypes, the brain region or brain network coupling feature analysis can be grouped and compared at the event phenotype level. This allows us to examine which brain regions have higher or lower signals before rapid propagation when they are both peak events, and also to examine which brain networks are correlated with higher underlying peaks under the same speed conditions. This can reveal the regional and network coupling features that cannot be distinguished by the entire correlation analysis.

[0063] Understandably, by extracting signal features of each brain region or brain network with the peak time of the candidate inflow event as the zero point, all events have a consistent time reference system on the time axis. The brain region responses between different events can be compared in the same time frame, avoiding the blurring of coupling features caused by the mixing of different event and non-event time points in the whole signal correlation analysis. This allows the extracted coupling features to accurately reflect the temporal correlation between cerebrospinal fluid inflow events and brain region activity.

[0064] In the specific implementation, after completing the detection, propagation confirmation, apparent velocity calculation and motion quality control of candidate inflow events, the signal features within a preset time window are extracted from the blood oxygen level dependent signal of the default mode network, with the peak time of each candidate inflow event as the zero point.

[0065] Specifically, refer to Figure 6 Part A (DMN event locking curve) uses the peak time of the lowest layer of cerebrospinal fluid as the zero point, i.e., the origin of the horizontal axis. The horizontal axis represents the time relative to that peak time, and the vertical axis represents the percentage value of the default mode network signal relative to the whole brain signal mean. Candidate inflow events are divided into fast-propagating event groups and slow-propagating event groups based on apparent flow velocity. The trajectories of default mode network signal changes in the two groups of events within the time range before and after the peak time are plotted using red and blue lines, respectively. By comparing the degree of separation between the two curves within the pre-event window, it is observed whether there is a signal increase in fast-propagating events before the event. Specifically, the signal mean within the 12-second to 8-second window before the event can be selected as the statistical comparison indicator. Figure 6 As shown in Part A, the network signal in the default mode of rapid cerebrospinal fluid propagation is higher during the preset window of 12 to 8 seconds before the event (i.e., -12 to -8 seconds).

[0066] refer to Figure 6 Part B of the study (DMN effect under different motion cleanup models) calculates the difference in the default mode network signal between the fast and slow groups within the pre-event window and its statistical significance (P-value) under the original condition of no motion regression. This comparison is then repeated sequentially after cleanup using a rigid body 6-parameter model, a rigid body 12-parameter model, a Friston-24 parameter model, and a Friston-24 model with an added signal intensity term, observing whether the effect direction remains positive and the trend of the P-value. The effect values ​​and P-values ​​under each model are summarized and output as the coupling feature of candidate inflow events at the default mode network level to verify the sensitivity of this coupling feature to different motion cleanup strategies.

[0067] Furthermore, step S5 also includes: Extract the signal value of the lowest layer of cerebrospinal fluid corresponding to the peak time of the candidate inflow event, and use the signal value as the original peak height; Extract the baseline signal value of the lowest layer of cerebrospinal fluid within a preset time window including the peak moment of the candidate inflow event; Calculate the difference between the original peak height and the signal baseline value, and use the difference as the peak significance of the candidate inflow event; Obtain the peak saliency set of all candidate inflow events within the collection operation to which the candidate inflow event belongs; Based on the cumulative distribution position of the peak significance in the set of peak significance, the peak significance is converted into a percentile value within the acquisition run, and the percentile value is used as the normalized peak value within the run of the candidate inflow event.

[0068] It should be noted that the raw peak height is the signal value of the lowest layer of cerebrospinal fluid corresponding to the peak time of the candidate inflow event. The signal baseline value is the reference level of the lowest layer of cerebrospinal fluid signal within a local time window near the peak time of the candidate inflow event. Peak significance is the vertical difference between the raw peak height and the signal baseline value, reflecting the prominence of the peak signal relative to the local baseline. An acquisition run is a complete scanning phase of magnetic resonance imaging (MRI) data acquisition. The peak significance set is the set of peak significance values ​​for all candidate inflow events within the same acquisition run. The percentile value is the percentile value corresponding to the cumulative distribution position of the peak significance value within the total peak significance set of the entire acquisition run.

[0069] It is understandable that the original peak height is greatly affected by factors such as global signal drift and differences in signal scale between scans. Peak significance, by subtracting the local baseline value, extracts the degree of prominence of the peak relative to the surrounding signal level, which better reflects the signal enhancement effect of the incoming event itself. Therefore, by introducing the concept of peak significance to replace the original signal amplitude as a measure of peak size, the sensitivity to slow baseline drift can be effectively reduced, making the comparison of peak size across events and across operations more reliable.

[0070] Understandably, different acquisition protocols and signal scales may affect the direct comparison of raw peak heights or peak significance. Converting peak significance to percentile values ​​within the acquisition run can reduce the impact of overall signal scale differences between runs and improve the comparability of event ranking; this conversion does not constitute a calibration of the absolute cerebrospinal fluid inflow.

[0071] In the specific implementation, refer to Figure 7(Event-level definition of peak size of the lowest layer cerebrospinal fluid) After standardizing the time series of the lowest layer cerebrospinal fluid z1, candidate inflow events are identified using a local peak detection algorithm. For each candidate inflow event i, the standardized signal value of z1 corresponding to its peak time is extracted as the original peak height A(i), and the local contour of the z1 signal is extracted within a preset time window centered on the peak time. The mean or median of the signal value within this local contour is calculated as the baseline value b(i). The vertical difference between the original peak height A(i) and the baseline value b(i) is used as the peak significance P(i) of the event, and P(i) is used as the main peak size measure of the event. At the same time, the half-peak significance width W(i), that is, the width value corresponding to half the height of the peak at P(i), and the peak area above the baseline AUC(i), that is, the integral area of ​​the peak curve above the local baseline b(i), are recorded as auxiliary peak shape features. When cross-event or inter-group comparisons are required, the peak significance set of all candidate inflow events within the same acquisition run is obtained, and the percentile value corresponding to the cumulative distribution position of the peak significance of each event in the set is calculated as the percentile peak size within the run, so as to eliminate the influence of different acquisition protocols and differences in overall signal scale on peak size comparison.

[0072] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the method for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. Any simple modifications based on this technical concept are within the scope of protection of this application.

[0073] This application also provides a device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. Please refer to [reference needed]. Figure 8 The device for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks includes: The acquisition module 10 is used to acquire functional magnetic resonance imaging (fMRI) data to be processed. The fMRI data is acquired with a repetition time of no more than 1.0 second and simultaneously covers the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and at least one brain region in the same scan. The calculation module 20 is used to determine candidate inflow events in the lowest layer of cerebrospinal fluid based on the functional magnetic resonance data, confirm the positive delayed propagation of the candidate inflow events in the adjacent upper layer of cerebrospinal fluid, obtain the candidate propagation delay, and calculate the apparent flow velocity of the candidate inflow events based on the actual interlayer distance between the two layers of cerebrospinal fluid and the candidate propagation delay. Alignment module 30 is used to align signals of different brain regions or brain networks in the functional magnetic resonance data based on the occurrence time of the candidate inflow event, and extract the coupling features of the candidate inflow event from the aligned functional magnetic resonance data. Output module 40 is used to take the apparent flow rate and coupling characteristics of the candidate inflow event as measurement results.

[0074] The cerebrospinal fluid (CSF) event-level flow velocity coupled with brain regions and brain networks provided in this application employs the CSF event-level flow velocity coupled measurement method described in the above embodiments. This solves the technical problem of not being able to simultaneously achieve the measurement of apparent flow velocity between CSF signal layers and the extraction of coupling features of brain regions and brain networks with the accuracy of a single physiological event. Compared with the prior art, the beneficial effects of the CSF event-level flow velocity coupled measurement method provided in this application are the same as those of the CSF event-level flow velocity coupled measurement method described in the above embodiments. Furthermore, other technical features of the CSF event-level flow velocity coupled measurement method are the same as those disclosed in the above embodiments, and will not be repeated here.

[0075] This application provides a device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. The device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the coupling measurement method of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in the first embodiment described above.

[0076] The following is for reference. Figure 9 This illustration shows a schematic diagram of a structure suitable for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in implementing embodiments of this application. The measurement device for coupling cerebrospinal fluid event-level flow velocity with brain regions and brain networks in embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, personal digital assistants, tablets, portable multimedia players, and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 9 The illustrated device for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0077] like Figure 9As shown, the device for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in ROM (Read Only Memory) 1002 or a program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the device for measuring the coupling of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. I / O (input / output) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the cerebrospinal fluid event-level flow velocity and brain region and brain network coupling measurement device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a cerebrospinal fluid event-level flow velocity and brain region and brain network coupling measurement device with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0078] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0079] The cerebrospinal fluid (CSF) event-level flow velocity coupled with brain regions and brain networks provided in this application employs the CSF event-level flow velocity coupled measurement method described in the above embodiments. This solves the technical problem of not being able to simultaneously achieve the measurement of apparent flow velocity between CSF signal layers and the extraction of coupling features of brain regions and brain networks with the precision of a single physiological event. Compared with the prior art, the beneficial effects of the CSF event-level flow velocity coupled measurement method provided in this application are the same as those of the CSF event-level flow velocity coupled measurement method provided in the above embodiments. Furthermore, other technical features of this CSF event-level flow velocity coupled measurement method are the same as those disclosed in the previous embodiment, and will not be repeated here.

[0080] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0082] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the coupling measurement method of cerebrospinal fluid event-level flow velocity with brain regions and brain networks in the above embodiments.

[0083] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0084] The aforementioned computer-readable storage medium may be included in a device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks; or it may exist independently and not assembled into a device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks.

[0085] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the coupling measurement device for cerebrospinal fluid event-level flow velocity and brain regions and brain networks, the coupling measurement device for cerebrospinal fluid event-level flow velocity and brain regions and brain networks implements the coupling measurement method for cerebrospinal fluid event-level flow velocity and brain regions and brain networks provided in the above embodiments.

[0086] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0087] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0088] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0089] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks. This solves the technical problem of not being able to simultaneously achieve the measurement of apparent flow velocity between cerebrospinal fluid signal layers and the extraction of coupling features of brain regions and brain networks with the accuracy of a single physiological event. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the coupling measurement method for cerebrospinal fluid event-level flow velocity with brain regions and brain networks provided in the above embodiments, and will not be repeated here.

[0090] The above description is only a part of the embodiments of this application and does not limit the scope of protection of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the scope of protection of this application.

Claims

1. A method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks, characterized in that, The method includes: Acquire functional magnetic resonance imaging (fMRI) data to be processed. The fMRI data is acquired with a repetition time of no more than 1.0 second and simultaneously covers the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and at least one brain region in the same scan. Based on the functional magnetic resonance imaging (fMRI) data, candidate inflow events in the lowest layer of cerebrospinal fluid are identified; the positive delayed propagation of the candidate inflow events is confirmed in the adjacent upper layer of cerebrospinal fluid to obtain the candidate propagation delay; the apparent velocity of the candidate inflow events is calculated based on the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid and the candidate propagation delay. Based on the occurrence time of the candidate inflow events, the signals of different brain regions or brain networks in the functional magnetic resonance imaging (fMRI) data are aligned, and the coupling features of the candidate inflow events are extracted from the aligned fMRI data. The apparent flow rate and coupling characteristics of the candidate inflow events are used as measurement results; The step of confirming the positive delayed propagation of the candidate inflow event in the adjacent upper cerebrospinal fluid layer to obtain the candidate propagation delay, and calculating the apparent velocity of the candidate inflow event based on the actual interlayer distance between the lowest cerebrospinal fluid layer and the adjacent upper cerebrospinal fluid layer and the candidate propagation delay, further includes: Based on the time series of the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid after layer-time correction, positive delayed cross-correlation is calculated within the local event window of the candidate inflow event, and the time offset when the correlation value of the positive delayed cross-correlation reaches its maximum is obtained. The time offsets greater than 0 are used as candidate propagation delays, and it is determined whether the candidate propagation delays are less than or equal to a preset maximum delay threshold. When the candidate propagation delay is less than or equal to the maximum delay threshold, check whether there is a local peak signal in the adjacent upper cerebrospinal fluid within a preset time range after the peak time of the candidate inflow event; If the local peak signal exists and the maximum correlation value is greater than the preset correlation coefficient threshold, then it is confirmed that the candidate inflow event has positive delayed propagation in the adjacent upper cerebrospinal fluid. According to the formula Calculate the apparent flow rate of the candidate inflow event, where, This refers to the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid along the normal direction of the fluid layer. For the candidate propagation delay, Let be the apparent flow rate of the i-th propagation confirmation event.

2. The method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in claim 1, characterized in that, The step of confirming the positive delayed propagation of the candidate inflow event in the adjacent upper cerebrospinal fluid and obtaining the candidate propagation delay includes: Obtain the motion parameter sequence corresponding to the functional magnetic resonance data, and calculate the signal change sequence based on the functional magnetic resonance data; Based on the motion parameter sequence and the signal change sequence, the degree of motion-related signal anomaly within the peak time and neighboring time window of the candidate inflow event is evaluated to obtain the evaluation result. Based on the evaluation results, quality labels are added to the candidate inflow events; The quality label indicates a target inflow event that meets a preset motion quality standard, and based on the target inflow event, positive delayed propagation is confirmed in the adjacent upper cerebrospinal fluid to obtain candidate propagation delay.

3. The method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in claim 2, characterized in that, The steps for evaluating the degree of motion-related signal anomalies within the peak time and neighborhood time window of the candidate inflow event, based on the motion parameter sequence and the signal change sequence, and obtaining the evaluation results, include: Based on the motion parameter sequence and the signal change sequence, the frame-by-frame displacement FD corresponding to the peak moment of the candidate inflow event is calculated, and the signal change DVARS corresponding to the peak moment is calculated based on the functional magnetic resonance data. When the frame-by-frame displacement FD exceeds a preset displacement threshold or the signal change DVARS exceeds a preset change threshold, the corresponding candidate inflow event is marked as not meeting the preset motion quality standard. Calculate the maximum value of the frame-by-frame displacement FD and the maximum value of the signal change DVARS of the candidate inflow event within a preset time window; When the maximum value of the frame-by-frame displacement FD exceeds the preset displacement threshold or the maximum value of the signal change DVARS exceeds the preset change threshold, the corresponding candidate inflow event is marked as not meeting the preset motion quality standard.

4. The method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in claim 2, characterized in that, The step of evaluating the degree of motion-related signal anomaly within the peak time and neighborhood time window of the candidate inflow event based on the motion parameter sequence and the signal change sequence, and obtaining the evaluation result, further includes: A motion regression model is constructed based on the motion parameter sequence, and the functional magnetic resonance imaging data is signal corrected according to the motion regression model. Based on the signal change sequence, candidate inflow events in the lowest layer of cerebrospinal fluid are re-determined in the corrected functional magnetic resonance imaging data; The candidate inflow events before and after correction are matched according to the tolerance of peak time and neighborhood time window to determine the retention status of each candidate inflow event before correction, and the event retention rate before and after correction is calculated. Based on the event retention rate, unmatched candidate inflow events are marked as events with motion-related signal anomalies, and the event retention rate and the degree of motion-related signal anomalies are used as the evaluation results.

5. The method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in claim 1, characterized in that, The step of extracting coupling features of the candidate inflow events from aligned functional magnetic resonance data includes: Obtain the peak value and apparent flow rate of each candidate inflow event; Based on the apparent flow rate and the peak value, each candidate inflow event is classified into a different event phenotype; Using the peak moment of the candidate inflow event as the zero point, the signal features of each brain region or brain network within a preset time window are extracted from the functional magnetic resonance data. The signal features are grouped according to the event phenotype to obtain the signal feature grouping corresponding to each event phenotype; The signal features among the various signal feature groups are compared, and the comparison results are used as the coupling features of the candidate inflow events.

6. The method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in claim 5, characterized in that, The step of obtaining the peak magnitude of each of the candidate inflow events includes: Extract the signal value of the lowest layer of cerebrospinal fluid corresponding to the peak time of the candidate inflow event, and use the signal value as the original peak height; Extract the baseline signal value of the lowest layer of cerebrospinal fluid within a preset time window including the peak moment of the candidate inflow event; Calculate the difference between the original peak height and the signal baseline value, and use the difference as the peak significance of the candidate inflow event; Obtain the peak saliency set of all candidate inflow events within the collection operation to which the candidate inflow event belongs; Based on the cumulative distribution position of the peak significance in the set of peak significance, the peak significance is converted into a percentile value within the acquisition run, and the percentile value is used as the normalized peak value within the run of the candidate inflow event.

7. A device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks, characterized in that, The device includes: The acquisition module is used to acquire functional magnetic resonance imaging (fMRI) data to be processed. The fMRI data is acquired with a repetition time of no more than 1.0 second and simultaneously covers the lowest layer of cerebrospinal fluid, the adjacent upper layer of cerebrospinal fluid, and at least one brain region in the same scan. The calculation module is used to determine candidate inflow events in the lowest layer of cerebrospinal fluid based on the functional magnetic resonance imaging data; confirm the positive delayed propagation of the candidate inflow events in the adjacent upper layer of cerebrospinal fluid to obtain the candidate propagation delay; and calculate the apparent flow velocity of the candidate inflow events based on the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid and the candidate propagation delay. The alignment module is used to align the signals of different brain regions or brain networks in the functional magnetic resonance data based on the occurrence time of the candidate inflow event, and extract the coupling features of the candidate inflow event from the aligned functional magnetic resonance data. The output module is used to take the apparent flow rate and coupling characteristics of the candidate inflow event as the measurement result; The step of confirming the positive delayed propagation of the candidate inflow event in the adjacent upper cerebrospinal fluid layer to obtain the candidate propagation delay, and calculating the apparent velocity of the candidate inflow event based on the actual interlayer distance between the lowest cerebrospinal fluid layer and the adjacent upper cerebrospinal fluid layer and the candidate propagation delay, further includes: Based on the time series of the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid after layer-time correction, a positive delay cross-correlation is calculated within the local event window of the candidate inflow event, and the time offset when the correlation value of the positive delay cross-correlation reaches its maximum is obtained; the time offset greater than 0 is taken as the candidate propagation delay, and it is determined whether the candidate propagation delay is less than or equal to a preset maximum delay threshold; when the candidate propagation delay is less than or equal to the maximum delay threshold, it is checked whether there is a local peak signal in the adjacent upper layer of cerebrospinal fluid within a preset time range after the peak time of the candidate inflow event; if the local peak signal exists, and the maximum correlation value is greater than the preset correlation coefficient threshold, then it is confirmed that the candidate inflow event has positive delay propagation in the adjacent upper layer of cerebrospinal fluid; according to the formula Calculate the apparent flow rate of the candidate inflow event, where, This refers to the actual interlayer distance between the lowest layer of cerebrospinal fluid and the adjacent upper layer of cerebrospinal fluid along the normal direction of the fluid layer. For the candidate propagation delay, Let be the apparent flow rate of the i-th propagation confirmation event.

8. A device for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in any one of claims 1 to 6.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the method for coupling measurement of cerebrospinal fluid event-level flow velocity with brain regions and brain networks as described in any one of claims 1 to 6.

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