DTI-based intractable OAB patient brain network analysis method

Abstract

The invention discloses a DTI-based brain network analysis method for a refractory OAB patient, and relates to a method for applying diffusion tensor imaging (DTI) and graph theory analysis to explore a central nervous regulation mechanism of the refractory OAB patient. 43 cases of refractory OAB patients and 46 cases of matched healthy contrasts are selected for DTI scanning. The method comprises the following steps: evaluating an overactive bladder symptom score table (OABSS), an overactive bladder symptom questionnaire table (OAB-Q), a Hamilton anxiety scale (HAM-A) and a Hamilton depression scale (HAMD) of all subjects, and recording related clinical data. A DTI and graph theory analysis method is adopted to explore the change of global and local topological attributes of the brain structure network of the intractable OAB patient. And further performing brain network function connection analysis on the discovered differential brain region as a seed point.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical imaging, and in particular relates to a DTI-based brain network analysis method for refractory OAB patients. Background Art

[0002] Overactive bladder (OAB) is a complex clinical syndrome characterized by urinary urgency, frequency, and nocturia, with or without urge incontinence. The disease has distinct gender and age patterns, with a higher incidence in women than in men, and the incidence increases with age. The diagnosis of OAB relies primarily on a systematic clinical evaluation, including a detailed history, urinalysis, bladder diary, and urodynamic testing. Current first-line treatment options include behavioral interventions, anticholinergics, and β3-adrenergic receptor agonists. However, approximately 30%-40% of patients respond poorly to conventional treatment. If symptoms do not resolve after a long course of behavioral training or if treatment with an anticholinergic medication fails after 6-12 weeks (including inadequate symptom relief or intolerable adverse reactions), they progress to refractory OAB. Existing second-line treatment options include bladder injections of botulinum toxin, sacral neuromodulation, and transcutaneous electrical nerve stimulation, but these treatments have limitations such as inconsistent efficacy, high invasiveness, and a high risk of complications.

[0003] The urination function is finely regulated by a complex neural network: the pancreatic islets, anterior cingulate cortex, and prefrontal cortex are responsible for urine storage, while the medial prefrontal cortex, hypothalamus, and pontine micturition center regulate the urination process. This neural circuit is regulated by neurotransmitters such as dopamine, 5-HT, and acetylcholine. Resting-state functional magnetic resonance imaging (rs-fMRI) studies have found that OAB patients have functional abnormalities in multiple key brain regions (including the prefrontal cortex [5], pancreatic islets, anterior cingulate gyrus, and periaqueductal gray matter). At the same time, central nervous system dysfunction, whether caused by injury, neurodegenerative changes, or drug effects, may lead to bladder storage and urination dysfunction, which in turn causes symptoms such as frequent urination, urgency, and incontinence. Therefore, in-depth research on the characteristic changes in the central nervous system of patients with refractory OAB may provide new therapeutic ideas for breaking this vicious cycle of disease and emotion.

[0004] In recent years, the development of multimodal neuroimaging technology has provided a new perspective for exploring the pathological mechanisms of refractory OAB. Diffusion Tensor Imaging (DTI), a magnetic resonance imaging technology that detects white matter structure based on the diffusion properties of water molecules, can reveal the microstructural characteristics of brain networks. White matter is crucial in maintaining functional connectivity in the brain, and its abnormalities may be related to central nervous system dysfunction in patients with refractory OAB. This study included refractory OAB patients and healthy volunteers, constructed a graph theory-based brain structure network model using DTI, and analyzed the differences in network topological properties between the two groups of subjects. At the same time, combined with functional connectivity analysis, the abnormal patterns of brain structure and functional networks in OAB patients were systematically evaluated, thereby deepening the understanding of the pathological mechanisms of refractory OAB and central nervous system abnormalities. Summary of the Invention

[0005] The purpose of the present invention is to provide a DTI-based brain network analysis method for refractory OAB patients.

[0006] To achieve the above objectives, the present invention employs a DTI-based brain network analysis method for refractory OAB patients. This method involves both refractory OAB patients and healthy volunteers. Using DTI, a graph-theory-based brain structural network model is constructed to analyze differences in network topology between the two groups. Furthermore, combined with functional connectivity analysis, this method systematically assesses abnormalities in brain structure and functional networks in OAB patients, thereby deepening our understanding of the pathological mechanisms of refractory OAB and central nervous system abnormalities.

[0007] Furthermore, a DTI-based brain network analysis method for refractory OAB patients includes the following steps:

[0008] (1) Subject screening and data collection: A refractory OAB patient group and a demographically matched healthy control group were selected using the frequency matching method. 3D T1-weighted images and DTI sequence data, including MP-RAGE sequences, were acquired using a 3.0T MRI device.

[0009] (2) Data preprocessing: The mrtrix toolkit was used for DWI noise reduction, ring artifact removal, head motion and eddy current correction, and the N4 algorithm of ANTs was combined for bias field correction. The fmriprep and xcp_d toolkits were used for structural image registration to MNI space and functional data preprocessing.

[0010] (3) Fiber tracking analysis: A deterministic fiber tracking algorithm with integrated enhanced tracking strategy was used, with anisotropy thresholds randomly selected, fiber steering angle thresholds ranging from 15° to 90°, and a step size of 1 mm. Fiber bundles with lengths <30 mm or >300 mm were excluded.

[0011] (4) Graph theory index calculation: Based on the AAL90 graph, a structural connectivity matrix is ​​constructed to calculate global attributes including small-world index (Sigma), clustering coefficient (Cp), global efficiency (Eg), and local efficiency (Eloc), as well as node attributes including node efficiency (Ne), betweenness centrality (Bc), and degree centrality (Dc);

[0012] (5) Statistical comparative analysis: A GLM model with covariate correction was used to test inter-group differences. The global index used a P < 0.05 threshold, the node index applied BHFDR multiple correction, and the functional connectivity analysis used AlphaSim correction.

[0013] Furthermore, the frequency matching method specifically includes:

[0014] Matching variables were age, sex, and education level, and balance between groups was achieved through random selection or stratified sampling;

[0015] The sample size ratio of the control group to the study group was 1:1 to 1:1.5.

[0016] Furthermore, the MRI scanning parameters in step (1) include:

[0017] MP-RAGE sequence parameters: TR = 2200 ms, TE = 2.48 ms, TI = 900 ms, flip angle 8°, slice thickness 1 mm;

[0018] DTI sequence parameters: TR = 7900 ms, TE = 95 ms, b value = 0 / 1000, 30 diffusion directions, MDDW mode.

[0019] Furthermore, the pretreatment in step (2) specifically includes:

[0020] ANTs N4 algorithm was used to eliminate low-frequency intensity unevenness in MRI images;

[0021] Use the dwifslpreproc command to perform FSL head motion correction with BVector correction;

[0022] Regression with 36 confounding variables was applied;

[0023] 0.01-0.08 Hz band-pass filtering was used with a 6 mm Gaussian kernel for spatial smoothing.

[0024] Furthermore, in step (3), the fiber tracking settings are:

[0025] A staged random parameter optimization strategy was adopted, and the anisotropy threshold was randomly selected between 0.1 and 0.3;

[0026] Generate 10,000,000 seed points using the Monte Carlo method;

[0027] The fiber steering angle is dynamically optimized, with the front cycle adopting 15°-45° and the rear cycle adopting 45°-90°.

[0028] Furthermore, the network indicator calculation in step (4) includes:

[0029] The normalized small-world index was calculated based on 1000 random network generations;

[0030] The node efficiency is calculated by summing the inverse of the weighted shortest path;

[0031] Betweenness centrality is normalized by the proportion of nodes participating in the shortest path.

[0032] Furthermore, the statistical analysis method in step (5) includes:

[0033] The Wilcoxon rank sum test was used for non-normally distributed data;

[0034] The FDR-corrected mixed-effect model was used to analyze the differences in node attributes;

[0035] Functional connectivity was compared between groups using voxel-level inference with FWE correction.

[0036] Furthermore, the method also includes: using brain regions with significant differences as seed points to calculate functional connectivity analysis of the temporal signal correlation of whole-brain voxels, specifically including:

[0037] Extract the average BOLD signal of the seed point;

[0038] Calculate the whole-brain voxel Pearson correlation coefficient;

[0039] Fisher z transformation was applied to normalize the connection strength.

[0040] Furthermore, in step (4), the graph theory index calculation and node attribute differences are determined by the following steps:

[0041] Gender, age, and years of education were included as covariates;

[0042] The node index was considered to be a significantly different brain region if P < 0.05 after BHFDR correction.

[0043] Furthermore, the statistical analysis method in step (5) includes:

[0044] Normally distributed data were analyzed using the two-sample t test, and non-normally distributed data were analyzed using the Wilcoxon rank sum test;

[0045] The differences in brain network functional connectivity were corrected using the AlphaSim method of SPM12, and P < 0.05 after correction was considered significant.

[0046] A system for implementing a DTI-based brain network analysis method for refractory OAB patients, comprising:

[0047] Sample matching module: executes frequency matching algorithm to generate control group;

[0048] MRI scan control module: configure scan sequence and parameters;

[0049] Data processing module: Integrates mrtrix, FSL and ANTs toolkits to complete image preprocessing;

[0050] Network analysis module: calculates brain network topology properties based on Matlab and Gretna;

[0051] Statistical verification module: call SPSS, R and SPM12 to perform inter-group difference tests.

[0052] Graph theory analysis showed no significant differences in global attribute metrics between the two groups, but a significant difference in the node clustering coefficient (NCp) was observed in the right paracentral lobule (PCL.R) (P < 0.05). Functional connectivity analysis using the right paracentral lobule as the seed point revealed a significant decrease in functional connectivity between this region and the left inferior cerebellar peduncle (Cerebelum_Crus2_L) in the refractory OAB group (P < 0.05). Differences in the brain structural network between refractory OAB patients and healthy controls were primarily observed in the right paracentral lobule, where functional connectivity with the left inferior cerebellar peduncle was significantly reduced. These changes in structural network topology may be closely related to the pathogenesis of refractory OAB. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A flowchart for constructing a brain white matter structural network based on DTI data is presented. The flowchart shows the process of brain imaging and connectome analysis: starting with obtaining DTI cross-sectional-b0 images, reconstructing white matter fiber bundles based on DWI data, dividing brain regions using the AAL90 brain atlas, constructing a functional connectivity matrix heat map (color represents connection strength), and finally generating a brain network visualization diagram to show the connection structure within the brain.

[0054] Figure 2 Schematic diagram of brain regions with significant differences in Ncp between the refractory OAB group and the HC group. L: left; R: right. Ncp: node clustering coefficient. PCL.R: right paracentral lobule. Red areas indicate brain regions with significant differences between the two groups.

[0055] Figure 3Schematic diagram of the functional connectivity of the PCL_R brain region and the Cerebelum_Crus2_L brain region. A, three-view image of a prominent cluster; B, slice image of a prominent cluster. Cerebelum_Crus2_L: Left inferior cerebellar peduncle. The blue area represents the left inferior cerebellar peduncle. Functional connectivity between this region and the PCL.R brain region is significantly reduced in patients with refractory OAB. Colors represent T-scores; larger T-scores indicate more significant differences in functional connectivity. DETAILED DESCRIPTION

[0056] The present invention will be further described below with reference to the embodiments of the present invention and the accompanying drawings.

[0057] Example 1

[0058] Study subjects: This study recruited 43 patients with refractory OAB as the study group (OAB group) from the Urology Outpatient Department of Wuxi Second People's Hospital (Central Hospital Affiliated to Jiangnan University) from May to November 2024. A total of 46 healthy subjects who matched the study group in age, gender and education level were selected from the physical examination center of our hospital as the control group (HC group) using the frequency matching method. All subjects were independently diagnosed and confirmed by two senior urologists with extensive clinical experience in accordance with the "Guidelines for the Diagnosis and Treatment of Urological and Andrological Diseases in China" (2019 edition). The study collected detailed clinical data of all subjects, including demographic characteristics (gender, age, height, weight, education level) and disease-related information (disease duration, basic medical history, etc.). The following assessments were also performed: (1) a 72-hour continuous urination diary; (2) symptom severity was assessed using the Overactive Bladder Symptom Score (OABSS); and (3) quality of life was assessed using the Quality of Life Assessment in Bladder Disease (OAB-Q). This study protocol was approved by the Ethics Committee of Wuxi Second People's Hospital (approval number: 2024-Y-26), and all participants or their legal representatives signed informed consent.

[0059] 2. Inclusion criteria: (1) Han Chinese, right-handed, aged over 18 years; (2) met the diagnostic criteria for overactive bladder according to the guidelines, and excluded related diagnoses such as neurogenic bladder, bladder outlet obstruction, interstitial cystitis, and urinary incontinence; (3) had symptoms that did not improve after behavioral therapy and anticholinergic drug treatment (any anticholinergic drug used for ≥8 weeks); (4) had not taken anticholinergic drugs within 72 hours before MRI scanning; (4) signed the informed consent form.

[0060] 3. Exclusion criteria: (1) Pregnant or lactating women; (2) Previous history of urinary system or reproductive tract surgery; (3) Combined with severe physical illness or use of special anti-anxiety, antidepressant and other drugs in the past 3 months; (4) Family history of special genetic diseases (such as Alzheimer's disease); (5) Presence of other diseases that may cause urinary system symptoms (such as vaginitis); (6) Contraindications to MRI scanning (such as implanted electronic or metal devices); (7) T1WI showing obvious brain anatomical abnormalities, cerebral infarction or other vascular damage.

[0061] 4. MRI examination: A Siemens MAGNETOM Vida 3.0T MRI scanner equipped with a 32-channel flexible skull coil was used. To reduce head movement during the scan, both temporal regions of each subject were fixed using the head holder provided with the MRI device. All subjects were required to arrive at the MRI room 10 minutes before the start of the scan, empty their bladders before the scan, and remain relaxed and at rest during the scan. They were also required to lie flat, be quiet, close their eyes, and be awake without thinking. Plain MRI scans of all subjects were performed by professionally trained radiologists. Those who were screened and confirmed to have no obvious structural abnormalities continued with brain structural and functional imaging scans.

[0062] First, a rapid three-dimensional navigator imaging sequence was performed to obtain axial, coronal, and sagittal navigator images. Scan parameters were as follows: repetition time (TR) = 3.2 ms, echo time (TE) = 1.37 ms, flip angle = 8°, slice thickness = 1.6 mm, matrix size = 160 × 160, field of view (FOV) = 260 mm × 260 mm, and scan time = 14 seconds. The longitudinal fissure of the brain was perpendicular to the examination table. Subsequently, 3D T1-weighted images were acquired using a magnetization-prepared rapid acquisition gradient echo (MP-RAGE) sequence with the following parameters: TR = 2200 ms, TE = 2.48 ms, TI = 900 ms, flip angle = 8°, number of slices = 176, slice thickness = 1 mm, interslice spacing = 0 mm, matrix = 256 × 256, field of view = 230 mm × 230 mm, and scan time = 5 minutes and 38 seconds. After completion of the scan, motion artifacts due to head movement and wraparound artifacts due to incomplete scanning of the entire brain were eliminated. Finally, a DTI scan was performed, and an axial scan was performed with the anterior commissure-posterior commissure (AC-PC) line as the reference plane. The scanning parameters were: TR = 7900 ms, TE = 95 ms, number of slices = 54, slice thickness = 2 mm, matrix = 128 × 128, FOV = 220 mm × 220 mm, b value = 0 / 1000, number of directions = 30, diffusion mode = MDDW, and scanning time = 13 minutes and 51 seconds.

[0063] 5. Data preprocessing: DICOM files were converted to NIfTI files using dcm2niix. DWI preprocessing was performed using the mrtrix toolkit (https: / / www.mrtrix.org / ) by first performing data noise reduction and then removing ring artifacts. The dwifslpreproc command of mrtrix (based on FSL,

[0064] Head motion and eddy current corrections were performed using the MRI software (https: / / fsl.fmrib.ox.ac.uk / fsl / fslwiki / ) and the BVector was adjusted accordingly. Finally, the N4 algorithm from ANTs was applied for bias field correction to eliminate low-frequency intensity inhomogeneities in the MRI images.

[0065] Brain network connectivity preprocessing was performed using the fmriprep and xcp_d toolkits. The fmriprep pipeline includes temporal slice correction, motion correction, structural image registration, and image normalization to MNI152NLin6Asym space (2 mm resolution). The xcp_d pipeline includes regression of 36 confounding variables (including six motion parameters and their temporal derivatives, the mean of the whole brain / white matter / cerebrospinal fluid signals and their temporal derivatives, and the squared terms of these parameters), spike removal, bandpass filtering (0.01-0.08 Hz), and finally spatial smoothing using a Gaussian kernel with a 6 mm full width half maximum (FWHM) width.

[0066] 6. Fiber tracking: This study used a deterministic fiber tracking algorithm

[10] for analysis and integrated an enhanced tracking strategy

[11] to optimize the repeatability of the results. The tracking parameters were set as follows: the anisotropy threshold was randomly selected, and the fiber steering angle threshold was randomly set between 15° and 90° with a step size of 1 mm. To ensure the accuracy of fiber tracking, fiber bundles with lengths less than 30 mm or greater than 300 mm were excluded. A total of 10,000,000 seed points were set during the tracking process.

[0067] 7. Indicator Calculation: Using brain regions in the AAL90 atlas as nodes, a structural connectivity matrix was constructed using the number of fibers between two ROIs for subsequent analysis. Using the Matlab platform and its toolkit, Gretna, weighted brain networks were constructed and topological properties analyzed. Global attributes (one value per network) and node attributes (one value per node) were calculated. Global attributes included the small-world index (Sigma), which assesses the network's smallness; the clustering coefficient (Cp), which reflects the degree of node clustering and local connection density; the global efficiency (Eg), which measures the network's information transmission efficiency; the local efficiency (Eloc), which reflects the information processing capacity of each subnetwork; and the shortest path (Lp), which represents the average distance of information transmission within the network. Node attributes included node efficiency (Ne), node clustering coefficient (NCp), node local efficiency (NLe), betweenness centrality (Bc), degree centrality (Dc), and node shortest path (NLp). The small-world index was calculated based on 1000 random network generation runs. After identifying the differential brain regions, the differential brain regions were used as seed points, and the Pearson correlation between each voxel and the average time signal within the seed point was calculated. The correlation values ​​were then processed with Fisher z transformation. Figure 1 .

[0068] 8. Statistical analysis: SPSS software (version 20.0, IBM, New York, USA) was used for statistical analysis. Normally distributed quantitative data were expressed as mean ± standard deviation ( ) and were analyzed using a two-sample t-test. Enumeration data were expressed as numbers or percentages and were analyzed using Fisher's exact test. Non-normally distributed quantitative data were expressed as medians (first to third quartiles) and analyzed using the Wilcoxon rank-sum test. P < 0.05 was considered statistically significant. Graph-based metrics were analyzed using a general linear model in R software with a two-sample t-test, with gender, age, and years of education included as covariates. Global metrics were considered significant when P < 0.05; nodal metrics were corrected for multiple comparisons using the Benjamini-Hochberg False Discovery Rate (BHFDR) method, with P < 0.05 considered significant. The functional connectivity analysis of brain networks was performed using the general linear model of SPM12 software (Wellcome Centre for Human Neuroimaging, London, UK) to perform between-group T-tests. Gender, age, and years of education were also used as covariates, and the AlphaSim method of DPABI was used for multiple comparison correction. After correction, a P value < 0.05 was considered to be significant.

[0069] result

[0070] 1. Clinical Data: As shown in Table 1, a total of 89 subjects were enrolled in this study, including 43 subjects in the refractory OAB group (12 males, 31 females, mean age, 53.56±16.68 years) and 46 subjects in the HC group (11 males, 35 females, mean age, 52.70±11.65 years). There were no significant differences between the two groups in demographic characteristics, including age, sex ratio, height, weight, body mass index (BMI), and years of education (P>0.05), indicating good baseline comparability between the two groups.

[0071] The refractory OAB group had significantly higher urination frequency during the day and night, OABSS score, OAB-Q1 score, OAB-Q2 score and urination-related indicators than the HC group (P < 0.001), reflecting the significant abnormalities in urination frequency and symptom severity in refractory OAB patients.

[0072] Table 1 Comparison of clinical data between OAB group and HC group

[0073]

[0074] 2. Differences in Global Graph Properties between the OAB and HC Groups: As shown in Table 2, no statistically significant differences were observed between the OAB and HC groups in any of these global attribute metrics (P>0.05). Regarding the directionality of the T values, the OAB group showed slightly higher Sigma (T=1.07), Cp (T=0.687), and Lp (T=0.83) than the HC group, while slightly lower Eg (T=-0.765) and Eloc (T=-0.598) than the HC group. This trend may suggest that while the organizational efficiency of brain networks in OAB patients is slightly reduced, the basic network topology is still maintained. The clinical significance of this trend requires further exploration.

[0075] Table 2. Differences in global indicators

[0076]

[0077] Note: Sigma: small-world index, Cp: clustering coefficient, Eg: global efficiency, Eloc: local efficiency, Lp: shortest path, OAB: refractory overactive bladder, HC: control group.

[0078] 3. Differences in graph-theoretic node attributes between the OAB and HC groups: Node attributes in graph-theoretic analysis refer to the characteristics or data associated with each node (or vertex) in a network. Ne refers to the efficiency of parallel information transmission within a given node within the network; NCp refers to the ratio of the actual number of edges between directly connected neighbors of a particular brain region to the maximum number of edges that could exist between those neighbors; NLe refers to the communication efficiency between a node's neighbors after the node is removed; Bc refers to the number of times a node serves as an intermediary point in the shortest paths between other pairs of nodes in the network; Dc refers to the number of connections a node has within the network; and NLp refers to the average length of the shortest paths from a given node to all other nodes in the network.

[0079] As shown in Table 3 and Figure 2 As shown in the figure, compared with the HC group, the OAB group only had a significant difference in NCp in the right paracentral lobule (PCL.R), while no statistically significant differences were found in the node properties of other brain regions. This suggests that although the overall brain network structure of OAB patients is not significantly changed, the local network connectivity of specific brain regions, such as the right PCL.R, may be affected in OAB.

[0080] Table 3

[0081]

[0082] Note: PCL.R: right paracentral lobule, OAB: refractory overactive bladder, HC: control group.

[0083] 4. Analysis of brain network functional connectivity with PCL.R region as seed point: as shown in Table 4 and Figure 3 As shown, further functional connectivity analysis using the PCL.R region as the seed point revealed significant functional connectivity differences in the left inferior cerebellar peduncle (Cerebelum_Crus2_L) (voxel-level P<0.001, cluster-level P<0.05, AlphaSim correction). This finding indicates that OAB patients experience a significant decrease in functional connectivity between the PCL.R region and Cerebelum_Crus2_L, possibly reflecting abnormalities in information transmission and integration between these two brain regions during the disease state, suggesting a potential mechanism for neural circuit dysfunction.

[0084] Table 4 Functional connectivity analysis of brain networks with PCL.R region as seed point

[0085]

[0086] Note: Cerebelum_Crus2_L: left inferior cerebellar peduncle.

[0087] discuss

[0088] Refractory OAB is a chronic disease that seriously affects the quality of life of patients. Although a variety of treatment options are currently available clinically, including behavioral intervention, drug therapy, and neuromodulation techniques, a considerable proportion of patients still do not respond well to these conventional therapies, and their symptoms persist or even worsen. These patients often experience core symptoms such as intractable urinary urgency, frequent urination, and nocturia. At the same time, they face physical and mental stress caused by repeated attacks and ineffective treatment, which in turn leads to psychological problems such as anxiety and depression, and seriously interferes with social activities, occupational performance, and family life. Given its complex disease characteristics and urgent treatment needs, in-depth research on the exact pathogenesis and potential pathophysiological basis of refractory OAB and identification of possible biological targets are crucial to promoting the development of innovative treatments and improving patient prognosis.

[0089] There are currently three main hypotheses regarding the pathophysiological mechanisms of OAB: myogenic, neurogenic, and urothelial. The neurogenic hypothesis has garnered significant attention in recent years due to its extensive research and robust evidence. This hypothesis posits that abnormalities in the nervous system play a central role in the pathogenesis of OAB. These abnormalities involve both peripheral nervous system dysfunction (such as abnormal pelvic nerve sensory conduction and sympathetic dysfunction) and central nervous system functional alterations (including the brainstem, cerebellum, cortex, and subcortical structures). Neuroimaging studies, particularly functional magnetic resonance imaging (fMRI), have shown that OAB patients exhibit abnormal activation patterns in brain regions such as the prefrontal cortex, insula, and cerebellum during bladder function. Animal experiments have successfully induced OAB-like symptoms through sacral nerve injury, providing direct evidence for this. Clinical observations have shown that patients with neurological diseases such as multiple sclerosis and spinal cord injury often suffer from OAB, and that symptoms are closely correlated with the location and severity of neurological damage. At the molecular level, studies have found that an imbalance in neurotransmitters (such as acetylcholine and norepinephrine) and abnormal expression of their receptors (such as the M3 cholinergic receptor) play a key role in the onset and persistence of symptoms. Notably, neuromodulatory therapies such as sacral nerve stimulation have achieved significant clinical results, further confirming that regulating neural signaling can effectively improve OAB symptoms. These multifaceted research evidence strongly supports the neurogenic hypothesis and provides an important theoretical basis for understanding the pathogenesis of refractory OAB and developing new treatment options.

[0090] The human micturition reflex is a highly complex neural control system involving precise coordination between the central and peripheral nervous systems. This process involves multiple key steps: When the bladder is full, mechanoreceptors in the bladder wall transmit signals via the sacral nerves (S2-S4) to the sacral spinal cord. After spinal integration, the signals ascend via the lateral spinothalamic tract to higher-order centers (including the prefrontal cortex, insula, and anterior cingulate gyrus), forming urinary awareness and control functions. As bladder pressure increases, the spinal cord reflex arc is activated, leading to sympathetic inhibition and parasympathetic excitation, which in turn promotes detrusor contraction and coordinates relaxation of the external urethral sphincter, ultimately completing urination. After urination, feedback signals from bladder wall receptors restore bladder pressure to baseline levels, preparing for the next urine storage cycle. Dysfunction of this sophisticated neural network can lead to urinary tract disorders, including obstructive urinary bladder (OAB) and urinary incontinence. Notably, this study, using DTI graph analysis, revealed for the first time that functional connectivity between the right paracentral lobule and the left inferior cerebellar peduncle was significantly reduced in patients with refractory OAB. This finding provides direct evidence for the key role of the central nervous system in the pathogenesis of refractory OAB. Dysfunction in these brain regions may contribute to the pathology of refractory OAB by affecting bladder perception, urge control, and emotional regulation.

[0091] The paracentral lobule, located on the medial surface of the parietal lobe, adjacent to the precentral and postcentral gyri, is a crucial center for integrating motor and sensory functions, playing a particularly crucial role in lower limb motor control and sensory processing. Anatomically, it is closely connected to regions that control autonomic functions, including bladder control. Studies have shown that the paracentral lobule interacts significantly with the primary motor cortex, which plays a key role in voluntary muscle control during urination. Clinical studies have reported that repetitive transcranial magnetic stimulation (rTMS) of the bilateral paracentral lobules significantly improves urinary awareness in patients with urinary incontinence and major vascular cognitive impairment, suggesting that stimulation of this area can enhance bladder control mechanisms. However, it is unclear whether, in addition to participating in bladder sensation and urination control, the paracentral lobule also couples with emotion regulation networks to integrate bladder afferent signals, the micturition reflex, and emotional responses.

[0092] The cerebellar peduncles are a crucial neural pathway connecting the cerebellum to the brainstem and are composed of the superior, middle, and inferior cerebellar peduncles. The inferior cerebellar peduncles, which connect the cerebellum to the medulla oblongata, in particular, have functions that extend beyond the traditional understanding of simple motor coordination. Through complex neural connections with the spinal cord and other central nervous system structures, the cerebellar peduncles regulate bladder motor function and play a key role in the precise process of urination. Studies have also shown that these structures participate in emotion regulation through interactions with the limbic system, thereby influencing the integration of emotional responses and related motor functions. This study observed altered functional connectivity in the left inferior cerebellar peduncles, suggesting that they may be a key structure in the central nervous system regulating the micturition reflex. Its dysfunction may be a key contributor to abnormal bladder fullness perception and hyperreflexia. It is important to examine whether this significant reduction in functional connectivity between the paracentral lobule region and the inferior cerebellar peduncles is prevalent in refractory OAB. Comparative analysis with other populations with lower urinary tract symptoms is also necessary to confirm the robustness of these findings.

[0093] Although this study revealed novel mechanisms of central nervous system dysfunction in refractory OAB using neuroimaging techniques, several limitations warrant attention. First, the relatively small sample size included in the study may affect the statistical power and generalizability of the results. Second, while brain network functional connectivity analysis provides important information for understanding the underlying functional associations between brain regions, it cannot directly reflect the dynamic brain functional changes and causal relationships during urination behavior. Future studies should expand the sample size and conduct multicenter validation to enhance the reliability and representativeness of the findings. Furthermore, further urination-related task-based fMRI studies are needed, combined with behavioral indicators, to more precisely characterize the dynamic activity characteristics and functional network reorganization patterns of various brain regions during urination regulation. These methodological improvements will help to more comprehensively elucidate the neuropathophysiological mechanisms of OAB.

[0094] In summary, this study, using functional imaging, for the first time identified abnormalities in central nervous system functional connectivity in patients with refractory OAB. It also discovered disturbances in functional connectivity between the paracentral lobule and the left inferior cerebellar peduncle, and proposed a new hypothesis, the "brain-bladder axis," which provides a new perspective on the potential pathogenesis of refractory OAB. This hypothesis emphasizes the critical role of the central nervous system in regulating urinary function and provides an important theoretical basis for the etiology of refractory OAB and its personalized intervention. In the future, research centered on the brain-bladder axis will not only help elucidate the pathophysiological mechanisms of OAB but also open new avenues for precision medicine, targeted therapy, and optimized patient management strategies, thereby bringing greater benefits to OAB patients.

Claims

1. A DTI-based brain network analysis method for refractory OAB patients, characterized by: The following steps are involved: (1) Subject screening and data collection: A refractory OAB patient group and a demographically matched healthy control group were selected using the frequency matching method. 3D T1-weighted images and DTI sequence data, including MP-RAGE sequences, were acquired using a 3.0T MRI device. (2) Data preprocessing: The mrtrix toolkit was used for DWI noise reduction, ring artifact removal, head motion and eddy current correction, and the N4 algorithm of ANTs was combined for bias field correction. The fmriprep and xcp_d toolkits were used for structural image registration to MNI space and functional data preprocessing. (3) Fiber tracking analysis: A deterministic fiber tracking algorithm with integrated enhanced tracking strategy was used, with anisotropy thresholds randomly selected, fiber steering angle thresholds ranging from 15° to 90°, and a step size of 1 mm. Fiber bundles with lengths <30 mm or >300 mm were excluded. (4) Graph theory index calculation: Based on the AAL90 graph, a structural connectivity matrix is ​​constructed to calculate global properties including small-world index, clustering coefficient, global efficiency, and local efficiency, as well as node properties including node efficiency, betweenness centrality, and degree centrality; (5) Statistical comparative analysis: A GLM model with covariate correction was used to test inter-group differences. The global index used a P < 0.05 threshold, the node index applied BHFDR multiple correction, and the functional connectivity analysis used AlphaSim correction.

2. A DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: The frequency matching method specifically includes: Matching variables were age, sex, and education level, and balance between groups was achieved through random selection or stratified sampling; The sample size ratio of the control group to the study group was 1:1 to 1:1.

5.

3. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: The MRI scanning parameters in step (1) include: MP-RAGE sequence parameters: TR = 2200 ms, TE = 2.48 ms, TI = 900 ms, flip angle 8°, slice thickness 1 mm; DTI sequence parameters: TR = 7900 ms, TE = 95 ms, b value = 0 / 1000, 30 diffusion directions, MDDW mode.

4. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: The pretreatment in step (2) specifically includes: ANTs N4 algorithm was used to eliminate low-frequency intensity unevenness in MRI images; Use the dwifslpreproc command to perform FSL head motion correction with BVector correction; Regression with 36 confounding variables was applied; 0.01-0.08 Hz band-pass filtering was used with a 6 mm Gaussian kernel for spatial smoothing.

5. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: Fiber tracking settings in step (3): A staged random parameter optimization strategy was adopted, and the anisotropy threshold was randomly selected between 0.1 and 0.3; Generate 10,000,000 seed points using the Monte Carlo method; The fiber steering angle is dynamically optimized, with the front cycle adopting 15°-45° and the rear cycle adopting 45°-90°.

6. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: The network indicator calculation in step (4) includes: The normalized small-world index was calculated based on 1000 random network generations; The node efficiency is calculated by summing the inverse of the weighted shortest path; Betweenness centrality is normalized by the proportion of nodes participating in the shortest path.

7. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: The statistical analysis method in step (5) includes: The Wilcoxon rank sum test was used for non-normally distributed data; The FDR-corrected mixed-effect model was used to analyze the differences in node attributes; Functional connectivity was compared between groups using voxel-level inference with FWE correction.

8. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: Also includes: The brain regions with significant differences were used as seed points to calculate the functional connectivity analysis of the temporal signal correlation of whole-brain voxels, including: Extract the average BOLD signal of the seed point; Calculate the whole-brain voxel Pearson correlation coefficient; Fisher z transformation was applied to normalize the connection strength.

9. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: In step (4), the graph theory index calculation and node attribute differences are determined by the following steps: Gender, age, and years of education were included as covariates; The node index was considered to be a significantly different brain region if P < 0.05 after BHFDR correction.

10. The DTI-based brain network analysis method for refractory OAB patients according to claim 1, characterized in that: The statistical analysis method in step (5) includes: Normally distributed data were analyzed using the two-sample t test, and non-normally distributed data were analyzed using the Wilcoxon rank sum test; The differences in brain network functional connectivity were corrected using the AlphaSim method of SPM12, and P < 0.05 after correction was considered significant.

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