Analysis of the Influence of Myodural Bridge Complex Classification on Cervical Spondylotic Myelopathy Based on Magnetic Resonance Imaging

doi:10.21203/rs.3.rs-4721717/v1

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Analysis of the Influence of Myodural Bridge Complex Classification on Cervical Spondylotic Myelopathy Based on Magnetic Resonance Imaging

https://doi.org/10.21203/rs.3.rs-4721717/v1

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Objective To classify myodural bridge complex (MDBC) in the posterior atlanto-occipital interspace (PAOiS) and posterior atlanto-axial interspace (PAAiS) in cervical spondylotic myelopathy (CSM) based on Magnetic Resonance Imaging (MRI), analyzing the effects of sex, age, spinal compression ratio(CR), space available for the cord༈SAC༉, and cervical sagittal balance༈CSB) parameters on the classification of MDBC in CSM, the aim is to provide imaging evidence for the functional research and clinical application of MDBC.

Methods Imaging data from 96 patients with CSM and 104 healthy adults were retrospectively selected, were evaluated by univariate analysis of factors and multi-factor analysis of factor Influencing the MRI Classification of MDBC in CSM .

Results The results showed significantly lower proportions of Type A and Type B MDBC in the CSM group than in the control group (P < 0.001), the proportions of type C and type D MDBC were greater than those in the control group (P < 0.05), and have a statistically significant correlation with age (P < 0.05), but not with sex (P > 0.05). Types C and D predominated in the MDBC classification in CSM, regardless of sex and age (P > 0.05). Parameters such as the mean subaxial cervical space available for the cord (MSCSAC), and mean subaxial cervical compression ration (MSCCR) significantly influenced the MDBC classification in CSM (P < 0.05), particularly for Types C and D. Sex and CSB did not affect MDBC classification.

Conclusion The MDBC classification in CSM predominantly showed Type C and Type D, regardless of age. MSCSAC and MSCCR are influencing factors of MDBC classification in CSM, particularly affecting Type C and Type D MDB, regardless of sex and CSB.

Myodural bridge complex

Cervical spondylotic myelopathy

Imaging classification

Compression ration

Space available for the cord

Cervical sagittal balance

Magnetic resonance imaging

Cervical spondylotic myelopathy (CSM)[1,2]is the most prevalent spinal cord disease in adults, characterized by neurological impairments in limbs, trunk, and sphincter muscles, along with atypical symptoms like headaches[3]. MRI technology enables precise measurement and spatial assessment of spinal cord compression in CSM, elucidating potential correlations between symptom severity and treatment prognosis[4,5,6]. The myodural bridge complex (MDBC), composed of fused myodural bridge (MDB) fibers from various sources, acts synergistically[7]. Feng X et al.[8]classified MDBC into Types A, B, C, and D based on its visibility in the PAOiS and PAAiS, correlating type characteristics with degenerative changes in the cervical spine. Degenerative changes in the cervical spine of patients with CSM often involve spinal canal narrowing and disc protrusion that compress the spinal cord. Parameters such as spinal cord compression ratio (CR)[9], space available for the cord (SAC)[10], and cervical sagittal balance (CSB)[11]are important in assessing the severity of CSM and may affect the categorization into MDBC types. This study utilizes MRI technology to analyze and measure CR, SAC, and CSB values in CSM, identifying factors that influence the classification of CSM into MDBC types. These findings provide a imaging foundation for studying the functional aspects and clinical implications of MDBC.

Study participants

Approval for this study was obtained from the Ethics Committee for Research of Basic Medical College of Dalian Medical University. 104 healthy adults were served as the control group including 60 males and 40 females (age range from 20 to 69 years with an average of 41.16 ± 11.12 years) and 96 cases diagnosed with CSM were served as the patient group(age range from 20 to 69 years with an average of 38.78 ± 13.88 years) participated in this study

Study methods

Scanning equipment and parameters. 1.5T MRI scanner (HDxt, GE Healthcare, USA) was used with a neck coil phased array. Sagittal T2WI parameters: TR: 2740.0 ms, TE: 120.0ms, number of slices: 11, slice thickness: 3 mm, matrix: 256 × 256, FOV: 240 mm × 240 mm.

MRI evaluation criteria for MDBC classification. Subjects were scanned in the supine position, utilizing a neck phased-array coil that covered the occipital and sub-occipital regions. MRI classification method of Feng X et al. for MDBC was employed[8]. On sagittal T2WI of the cervical spine, MDBC appears as areas of low signal intensity. High signal intensity indicates the presence of fat within the PAOiS and PAAiS, while flow void signals suggest the presence of blood vessels. MDBC is classified into four types (Type A, Type B, Type C, and Type D) based on its location (PAOiS and PAAiS) where it connects to the dura mater and its signal characteristics (Fig. 1; Fig. 2: Diagram of MDBC Classification).

Measurement of CSB in CSM. CSB is assessed using the techniques outlined by Permsak P et al.[11](Fig. 3).

(1) Occiput-cervical inclination: Measurements were conducted for the occiput-cervical inclination angles at C3, C4, and C5. This angle is formed by the connection between the posterior edge of the cervical vertebrae and the McGregor line, which extends from the posterior upper side of the hard palate to the midline at the posterior end of the occipital bone. ( Figure Ia).

(2) Occiput-C2 angle: The angle is formed between the McGregor line and a line parallel to the lower endplate of C2.(Figure IIb)

(3) Occiput-cervical distance: The shortest distance is between the external occipital protuberance and the upper horizontal line of the spinous process of the axis vertebra (Figure IIIc).

(4) Cervical lordosis measurement angle: The angle is formed by the line extending from the lower endplate of the axis vertebra to C7. Positive values indicate posterior alignment, whereas negative values indicate anterior alignment (Figure IVd).

(5) High cervical sagittal axis: The horizontal distance between the center of C2 and the posterior edge of the upper endplate of C7 is measured. The center of C2 is determined at the intersection point of a diagonal line within the C2 vertebral body. Positive values indicate the center of C2 is positioned anterior to the posterior edge of the C7 endplate, whereas negative values indicate it is located posterior to the posterior edge of the C7 endplate (Figure Ve).

Measurement of SAC and CR in CSM. In this study, the cervical spine was divided into five segments: C2-3, C3-4, C4-5, C5-6, and C6-7. The SAC, CR, MSCCR, and MSCSAC values were measured for each segment.

(1) SAC values were measured as parameters of intervertebral disc and spinal canal volume. The calculation method involved determining the spinal cord available space in each segment, which was calculated as the horizontal sagittal diameter of the vertebral canal minus the sagittal diameter of the spinal cord. MSCSAC was then obtained by averaging SAC values from all five discs, aiming to comprehensively assess SAC values for each patient. (Fig. 4A)

(2) CR value is calculated as the spinal cord compression ratio, which is defined as the minimum sagittal diameter of the spinal cord divided by the widest transverse diameter at the same level. MSCCR is based on the average CR values from all 5 discs, aiming to comprehensively assess CR conditions for each patient. ( Fig. 4B)

Statistical analyses

SPSS 27.0 was used. Descriptive statistics were applied to categorical data [n (%)], while metric data underwent normality testing using the Shapiro-Wilk test. Metric data conforming to or approximating a normal distribution was presented as mean ± SD. Differences in sex composition and MDBC type composition between control and patient groups were assessed using independent samples t-tests for age. Chi-square tests were employed to compare the distribution of MDBC types among CSM patients. ANOVA was utilized to compare normal indicators among CSM patients with different MDBC subtypes. For groups showing significant differences, pairwise comparisons were conducted using the S-N-K. Multifactorial analysis included multicategory multifactor logistic regression analysis where applicable. A significance level of P < 0.05 denoted statistical significance.

MDBC classifcation methods in CSM

A significant difference in MDBC classification was observed between the patient and control groups (P < 0.001). Type A and B MDBCs were less prevalent in the patient group compared to the control group, with type A being the least common (6 cases, 6.1%). Conversely, type C and type D MDBC were more prevalent, with type D comprising the highest proportion (59 cases, 60.2%). Age was significantly different between the two groups (P = 0.022), while sex was not significantly different (Table 1).

Table 1

Comparative Analysis of Indicator Differences between Control Group and Diseased Group
Indicator	Classification	Control group	Diseased group	Statistical value	P value
Gender	Male	60(57.7)	54(55.1)	0.138	0.711
	Female	44(42.3)	44(44.9)
MDBC classification	Type A	63(60.6)	6(6.1)	89.306	< 0.001*
	Type B	20(19.2)	11(11.2)
	Type C	12(11.5)	22(22.4)
	Type D	9(8.7)	59(60.2)
Age (years)		44.16 ± 9.76	40.20 ± 14.34	2.305	0.022*
Note: * indicates statistical significance

Univariate Analysis of Factors Influencing MDBC Classification in CSM

(1) There were greater proportions of CSM patients with type C and D MDBC, but the difference in sex or age was not statistically significant (P > 0.05). (Table 2)

Table 2

Univariate Analysis of the Influence of General Data on MDBC Type
Indicator	Classification	Type A	Type B	Type C	Type D	X²/F value	P value
Gender	Male	3(3)	6(6)	10(10)	30(30)	4.768	0.90
	Female	3(3)	5(5)	12(12)	29(29)
Age		40.0 ± 16.05	45.64 ± 14.71	39.77 ± 15.79	39.37 ± 13.71	0.592	0.622
Note: * indicates statistical significan

2) Significant statistical differences (P < 0.01) were observed when comparing SAC values in the C4-7 segments, CR values in the C3-7 segments, and MSCSAC and MSCCR values in relation to MDBC types in CSM. However, no significant association was found between SAC values in the C2-4 segments and CR values in the C2-3 segments with MDBC (P > 0.05) (Table 3).

2) There was no significant association found between the classification of MDBC in CSM patients and cervical sagittal balance (CSB) parameters (P > 0.05) (Table 4).

Table 3

Univariate Analysis of the Influence of CR and SAC Values in Cervical Spine Segments on MDBC Type
Indicator	Type A	Type B	Type C	Type D	X²/F value	P value
CR(C2-3)	0.57 ± 0.06	0.54 ± 0.06	0.53 ± 0.04	0.51 ± 0.06	6.422	0.15
CR(C3-4)	0.54 ± 0.07	0.49 ± 0.08	0.49 ± 0.06	0.45 ± 0.08	7.101	0.001*
CR(C4-5)	0.53 ± 0.1	0.43 ± 0.07	0.47 ± 0.07	0.43 ± 0.08	7.001	0.001*
CR(C5-6)	0.54 ± 0.1	0.43 ± 0.09	0.46 ± 0.08	0.42 ± 0.09	8.798	0.001*
CR(C6-7)	0.55 ± 0.06	0.46 ± 0.04	0.48 ± 0.08	0.45 ± 0.12	5.37	0.002*
MSCCR	0.55 ± 0.06	0.47 ± 0.06	0.49 ± 0.05	0.45 ± 0.07	12.966	< 0.001*
SAC(C2-3)	5.53 ± 1.17	5.43 ± 0.6	5.76 ± 0.86	5.17 ± 1.05	7.672	0.34
SAC(C3-4)	4.9 ± 1.22	3.64 ± 0.89	3.89 ± 0.84	3.61 ± 1.06	8.039	0.21
SAC(C4-5)	4.8 ± 1.26	3.41 ± 1.31	3.6 ± 1.07	3.39 ± 0.99	8.362	<0.01*
SAC(C5-6)	4.59 ± 1.33	3.27 ± 1.18	3.44 ± 1.05	3.03 ± 1.23	8.063	<0.001*
SAC(C6-7)	5.68 ± 1.27	4.45 ± 1.44	4.38 ± 1.31	3.78 ± 1.67	7.865	<0.001*
MSCSAC	5.14 ± 0.94	3.97 ± 0.96	4.01 ± 0.73	3.66 ± 0.94	13.632	<0.001*
Note: * indicates statistical significance

Table 4

Univariate Analysis of the Influence of Cervical Sagittal Parameters on MDBC Type
Indicator	Type A	Type B	Type C	Type D	X²/F value	P value
C3- Occipito-cervical inclination	93.76 ± 7.45	93.09 ± 7.33	91.35 ± 7.09	93.17 ± 6.77	0.634	0.595
C4-Occipito-cervical inclination	94.96 ± 7.22	91.64 ± 6.36	91.07 ± 7.25	92.17 ± 7.19	1.418	0.242
C5-Occipito-cervical inclination	97.87 ± 10	91.8 ± 8.83	93.34 ± 8.92	94.09 ± 9.17	1.467	0.229
Occipito -C2 angle	3.27 ± 9.05	5.53 ± 6.74	1.83 ± 9.93	0.66 ± 11.48	0.676	0.569
Occipito-cervical distance	28.24 ± 7.2	32.95 ± 9.08	32.17 ± 6.59	30.08 ± 7.02	1.777	0.157
Cervical lordosis measurement angle	-9.79 ± 10.06	6.58 ± 10.92	6.14 ± 10.92	-5.69 ± 8.53	0.859	0.466
High cervical sagittal vertical axis	13.02 ± 8.49	13.51 ± 5.79	12.27 ± 6.79	12.14 ± 4.25	0.167	0.919
Note: * indicates statistical significance

Multi-factor Analysis of Factors Influencing MDBC Classification in CSM Patients

All variables, including sex, age, SAC (C2-7) values, CR (C2-7) values, MSCSAC, MSCCR values, and CSB parameters, were utilized as independent factors in a multi-factor logistic regression analysis, with MDBC type in CSM patients as the dependent variable. Type A MDBC was used as the baseline category for model establishment. The results of the multi-category multi-factor logistic regression indicated that MSCSAC and MSCCR significantly influenced the likelihood of Type C and D MDBC (P < 0.05). Specifically, each 1 cm increase in MSCSAC was associated with reduced odds of Type C and D MDBC and increased odds of Type A MDBC. The odds ratio (OR) for Type C MDBC decreased to 0.333 times that of Type A MDBC (OR = 0.333, P = 0.016), and for Type D MDBC, it decreased to 0.483 times that of Type A MDBC (OR = 0.483, P = 0.01). Furthermore, each 1% increase in MSCCR was associated with decreased odds of Type D MDBC and increased odds of Type A MDBC. The odds ratio (OR) for Type D MDBC decreased to 0.013 times that of Type A MDBC (OR = 0.013, P = 0.008) (Table 5).

Table 5

Multivariate and multifactorial Logistic Regression Analysis of Factors Influencing MDBC Type in Cervical Spondylosis Patients
Type	Factor	β value	P value	OR value	95%CI
Type B	MSCSAC	-0.977	0.15	0.376	0.115～1.229
Type B	MSCCR	-16.146	0.085	9.722	0.01～9.346
Type C	MSCSAC	-1.100	0.016^*	0.333	0.136～0.813
Type C	MSCCR	-10.475	0.137	2.823	0.0028～28.445
Type D	MSCSAC	-1.265	0.01^*	0.483	0.107～0.743
Type D	MSCCR	-20.469	0.008^**	0.013	0.0003～0.004
Note: * indicates statistical significance

Prior research has validated the feasibility of MRI-based morphological studies on MDBC[8,12,13,14,15]. A recent MRI study reaffirmed the classification of human MDBC into four types: Type A, Type B, Type C, and Type D[8]. This study is the first quantitative analysis to assess the influence of different MDBC types on CSM based on MRI. The findings revealed that Type A MDBC predominated in the normal population at 60.6%, consistent with the findings of Feng X[8]. In contrast, Type C and Type D were more prevalent in CSM patients, with Type D being the most common at 60.2%, followed by Type C at 22.4%, and Type B at 11.2%. Type A had the lowest occurrence at 6.1%. The classification of MDBC in CSM did not correlate with age or sex. Literature suggests that the pathogenesis of CSM involves tension stress transmitted to the spinal cord through the dentate ligament[16]. Changes in dural tension may affect suboccipital muscle activity through sensory pathways, potentially influencing MDB. In experiments with mice, Song X et al.[17] found that neck stiffness could lead to MDB proliferation and hypertrophy, indicating the adaptability of MDBC fibers. They hypothesized that ongoing abnormal tension from CSM might contribute to MDB growth and hypertrophy. Further investigations are necessary to explore whether Type C and Type D MDBC are implicated in the development of CSM.

The CR is essential for assessing clinical symptoms and cerebrospinal fluid (CSF) flow velocity in CSM patients[9,18]. It measures spinal cord compression by comparing the spinal canal diameter to the transverse diameter of the spinal cord. A lower CR indicates more severe spinal cord injury, often leading to significant functional impairment. SAC is also crucial in CSM evaluation, predicting symptomatic spinal stenosis[10,19]. Spinal canal stenosis can impact CSF dynamics. MDBC is one of the sources of CSF dynamics[23,24], and we think that the effects of the CR and SAC values on CSF dynamics may influence MDBC. CSB is a significant risk factor in CSM, affecting muscle length changes, particularly in the suboccipital muscles, the suboccipital muscle contributes to the formation of MDBC[20,21,22]. Multi-factor analysis indicates that the MSCSAC and MSCCR values of CSM are factors influencing the MDBC MRI classification, the results on factors influencing CSM MDBC classification showed that MSCSAC and MSCCR are pivotal. In this study, the classification was affected by SAC values in the C4-7 segments, CR values in the C3-7 segments, MSCSAC, and MSCCR, but not by SAC values in the C2-4 segments or CR values in the C2-3 segments. The decreasing involvement order of CSM segments-C5-6, C6-7, C4-5, C3-4, and C2-C3[25]may explain why C2-4 segment SAC values and C2-3 CR values do not impact MDBC classification in CSM. Each 1.0 cm increase in MSCSAC was associated with a decrease in Type C and Type D MDBC incidence, while increasing Type A MDBC incidence. Type C MDBC incidence decreased to 0.33 times that of Type A MDBC, and Type D MDBC incidence decreased to 0.483 times that of Type A MDBC. Each 1.0% increase in MSCCR was associated with Type D MDBC incidence decreasing to 0.013 times that of Type A MDBC. The study underscores that the severity of cervical spinal cord compression in CSM influences variations in MDBC classification, although further research is needed to elucidate the underlying mechanisms.

Research has documented that CSM correlates with imbalances in CSB parameters, including advancing age, reduced range of motion in cervical lordosis angle measurements, and decreased minimum bending angle[26](Lin T, et al. 2021). However, findings from this study indicate no association between CSM and CSB parameters. This discrepancy may be due to the study's use of static images obtained from supine MRI scans, which tend to yield false negative results. Typically, cervical lordosis is more accurately assessed using standing MRI or standing X-ray, which provide better differentiation effects[27].

The MDBC classification in CSM showed Type C and Type D predominantly, regardless of age. MSCSAC and MSCCR are influencing factors of MDBC classification in CSM, particularly affecting Type C and Type D MDB, regardless of sex and CSB.

Limitations

Firstly, the limitations of conventional MRI resolution and slice thickness in this study may contribute to false negatives in detecting MDBC, potentially resulting in an increased incidence of Type A MDB. Utilizing high-resolution MRI or 3D MRI techniques could mitigate this issue. Additionally, this study gathered quantitative imaging measurements of CSM in the MDBC from static MR images, which potentially influenced the accuracy of data analysis. Future research could investigate MDBC classification in CSM with spinal cord involvement using dynamic MR sequences and appropriate post-processing methodologies. Lastly, the sample size used in this study was relatively limited. Subsequent studies could enhance reliability by expanding the sample size.

Acknowledgements

Not Applicable

Authors’ contributions

HNW, HJS, and QX contributed to the concept and design of the study. HSY, CL YPT and ZCN acquired the data. SBY, NZ, YYCand JFZ analyzed and interpreted the data. QX, HJS, SBY, and HNW conceived and illustrated the unrolled model. HSY and CL drafted the manuscript. QX, HJS, and HNW revised the manuscript critically. All authors approved and edited the manuscript. HSY and CL are co-first authors and contributed equally to the work. QX, HJS, and HNW are co-corresponding authors and contributed equally to the work. All authors read and approved the fnal manuscript.

Funding

Natural Science Foundation of China (32071184)

Availability of data and materials

The datasets used and/or analysed during the current study available from the

corresponding author on reasonable request.

Ethics approval and consent to participate

The study has been performed in accordance with the Declaration of Helsinki and has been approved by the Ethics Committee for Research of Basic Medical College of Dalian Medical University. All patients participated free willingly and with written informed consent to the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Author details

¹Department of Anatomy, College of Basic Medicine, Dalian Medical University, Dalian 116044, China

²The 967 Hospital of the Joint Logistics Support Force, Dalian 116021, China

³Postgraduate Training Base, the 967 Hospital of the Joint Logistics Support Force Jinzhou Medical University, Dalian 116021, China.

⁴Department of Radiotherapy, the 967 Hospital of the Joint Logistics Support Force, Dalian 116021, China.

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Analysis of the Influence of Myodural Bridge Complex Classification on Cervical Spondylotic Myelopathy Based on Magnetic Resonance Imaging

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Abstract

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Introduction

Material and Methods

Study participants

Study methods

Statistical analyses

Results

MDBC classifcation methods in CSM

Univariate Analysis of Factors Influencing MDBC Classification in CSM

Multi-factor Analysis of Factors Influencing MDBC Classification in CSM Patients

Dicussion

Conclusion

Limitations

Declarations

References

Additional Declarations

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